{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第 3 章 从零搭建神经网络\n",
    "\n",
    "该章节内容翻译自 [Victor Zhou](https://victorzhou.com/) 的博客 [Machine Learning for Beginners: An Introduction to Neural Networks](https://victorzhou.com/blog/intro-to-neural-networks/)，已获得作者授权。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 神经元的实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9990889488055994\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def sigmoid(x: np.ndarray) -> np.ndarray:\n",
    "    # Sigmoid 激活函数: f(x) = 1 / (1 + e^(-x))\n",
    "    return 1 / (1 + np.exp(-x))\n",
    "\n",
    "class Neuron:\n",
    "    def __init__(self, weights: np.ndarray, bias: np.ndarray):\n",
    "        self.weights = weights\n",
    "        self.bias = bias\n",
    "\n",
    "    def feedforward(self, inputs: np.ndarray) -> np.ndarray:\n",
    "        # 点积权重和输入, 添加偏差\n",
    "        total = np.dot(self.weights, inputs) + self.bias\n",
    "        # 使用激活函数处理\n",
    "        return sigmoid(total)\n",
    "\n",
    "weights = np.array([0, 1]) # w1 = 0, w2 = 1\n",
    "bias = 4                   # b = 4\n",
    "n = Neuron(weights, bias)\n",
    "\n",
    "x = np.array([2, 3])       # x1 = 2, x2 = 3\n",
    "print(n.feedforward(x))    # 0.9990889488055994"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 神经网络的实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7216325609518421\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# 神经元的定义代码部分\n",
    "class OurNeuralNetwork:\n",
    "    \"\"\"\n",
    "    包含以下层的神经网络:\n",
    "    - 2 个输入\n",
    "    - 1 个包含 2 个神经元（h1, h2）的隐藏层\n",
    "    - 1 个包含 1 个神经元（o1）的输出层\n",
    "    所有的神经元有同样的权重和偏差：\n",
    "    - w = [0, 1]\n",
    "    - b = 0\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        weights = np.array([0, 1])\n",
    "        bias = 0\n",
    "\n",
    "        # 神经元（Neuron）类来自之前的定义\n",
    "        self.h1 = Neuron(weights, bias)\n",
    "        self.h2 = Neuron(weights, bias)\n",
    "        self.o1 = Neuron(weights, bias)\n",
    "\n",
    "    def feedforward(self, x: np.ndarray):\n",
    "        # 使用隐藏层1（h1）前向传播 x，得到结果 out_h1\n",
    "        out_h1 = self.h1.feedforward(x)\n",
    "        # 使用隐藏层2（h2）前向传播 x，得到结果 out_h2\n",
    "        out_h2 = self.h2.feedforward(x)\n",
    "\n",
    "        # o1 输出层的输入来自 h1 和 h2 的输出\n",
    "        # out_o1 为 o1 层前向传播后结果\n",
    "        out_o1 = self.o1.feedforward(np.array([out_h1, out_h2]))\n",
    "\n",
    "        return out_o1\n",
    "\n",
    "network = OurNeuralNetwork()\n",
    "x = np.array([2, 3])\n",
    "print(network.feedforward(x))  # 0.7216325609518421"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 均方误差的实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def mse_loss(y_true: np.ndarray, y_pred: np.ndarray) -> np.float:\n",
    "    # y_true 是 y_pred 同样形状的 Numpy 数组\n",
    "    return ((y_true - y_pred) ** 2).mean()\n",
    "\n",
    "y_true = np.array([1, 0, 0, 1])\n",
    "y_pred = np.array([0, 0, 0, 0])\n",
    "\n",
    "print(mse_loss(y_true, y_pred)) # 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 完整的代码实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0 loss: 0.233\n",
      "Epoch 10 loss: 0.084\n",
      "Epoch 20 loss: 0.048\n",
      "Epoch 30 loss: 0.037\n",
      "Epoch 40 loss: 0.031\n",
      "Epoch 50 loss: 0.028\n",
      "Epoch 60 loss: 0.025\n",
      "Epoch 70 loss: 0.023\n",
      "Epoch 80 loss: 0.021\n",
      "Epoch 90 loss: 0.019\n",
      "Epoch 100 loss: 0.018\n",
      "Epoch 110 loss: 0.017\n",
      "Epoch 120 loss: 0.016\n",
      "Epoch 130 loss: 0.015\n",
      "Epoch 140 loss: 0.014\n",
      "Epoch 150 loss: 0.013\n",
      "Epoch 160 loss: 0.013\n",
      "Epoch 170 loss: 0.012\n",
      "Epoch 180 loss: 0.011\n",
      "Epoch 190 loss: 0.011\n",
      "Epoch 200 loss: 0.010\n",
      "Epoch 210 loss: 0.010\n",
      "Epoch 220 loss: 0.010\n",
      "Epoch 230 loss: 0.009\n",
      "Epoch 240 loss: 0.009\n",
      "Epoch 250 loss: 0.009\n",
      "Epoch 260 loss: 0.008\n",
      "Epoch 270 loss: 0.008\n",
      "Epoch 280 loss: 0.008\n",
      "Epoch 290 loss: 0.007\n",
      "Epoch 300 loss: 0.007\n",
      "Epoch 310 loss: 0.007\n",
      "Epoch 320 loss: 0.007\n",
      "Epoch 330 loss: 0.007\n",
      "Epoch 340 loss: 0.006\n",
      "Epoch 350 loss: 0.006\n",
      "Epoch 360 loss: 0.006\n",
      "Epoch 370 loss: 0.006\n",
      "Epoch 380 loss: 0.006\n",
      "Epoch 390 loss: 0.006\n",
      "Epoch 400 loss: 0.005\n",
      "Epoch 410 loss: 0.005\n",
      "Epoch 420 loss: 0.005\n",
      "Epoch 430 loss: 0.005\n",
      "Epoch 440 loss: 0.005\n",
      "Epoch 450 loss: 0.005\n",
      "Epoch 460 loss: 0.005\n",
      "Epoch 470 loss: 0.005\n",
      "Epoch 480 loss: 0.005\n",
      "Epoch 490 loss: 0.004\n",
      "Epoch 500 loss: 0.004\n",
      "Epoch 510 loss: 0.004\n",
      "Epoch 520 loss: 0.004\n",
      "Epoch 530 loss: 0.004\n",
      "Epoch 540 loss: 0.004\n",
      "Epoch 550 loss: 0.004\n",
      "Epoch 560 loss: 0.004\n",
      "Epoch 570 loss: 0.004\n",
      "Epoch 580 loss: 0.004\n",
      "Epoch 590 loss: 0.004\n",
      "Epoch 600 loss: 0.004\n",
      "Epoch 610 loss: 0.004\n",
      "Epoch 620 loss: 0.004\n",
      "Epoch 630 loss: 0.003\n",
      "Epoch 640 loss: 0.003\n",
      "Epoch 650 loss: 0.003\n",
      "Epoch 660 loss: 0.003\n",
      "Epoch 670 loss: 0.003\n",
      "Epoch 680 loss: 0.003\n",
      "Epoch 690 loss: 0.003\n",
      "Epoch 700 loss: 0.003\n",
      "Epoch 710 loss: 0.003\n",
      "Epoch 720 loss: 0.003\n",
      "Epoch 730 loss: 0.003\n",
      "Epoch 740 loss: 0.003\n",
      "Epoch 750 loss: 0.003\n",
      "Epoch 760 loss: 0.003\n",
      "Epoch 770 loss: 0.003\n",
      "Epoch 780 loss: 0.003\n",
      "Epoch 790 loss: 0.003\n",
      "Epoch 800 loss: 0.003\n",
      "Epoch 810 loss: 0.003\n",
      "Epoch 820 loss: 0.003\n",
      "Epoch 830 loss: 0.003\n",
      "Epoch 840 loss: 0.003\n",
      "Epoch 850 loss: 0.003\n",
      "Epoch 860 loss: 0.003\n",
      "Epoch 870 loss: 0.002\n",
      "Epoch 880 loss: 0.002\n",
      "Epoch 890 loss: 0.002\n",
      "Epoch 900 loss: 0.002\n",
      "Epoch 910 loss: 0.002\n",
      "Epoch 920 loss: 0.002\n",
      "Epoch 930 loss: 0.002\n",
      "Epoch 940 loss: 0.002\n",
      "Epoch 950 loss: 0.002\n",
      "Epoch 960 loss: 0.002\n",
      "Epoch 970 loss: 0.002\n",
      "Epoch 980 loss: 0.002\n",
      "Epoch 990 loss: 0.002\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "def sigmoid(x: np.ndarray) -> np.ndarray:\n",
    "    # Sigmoid 激活函数: f(x) = 1 / (1 + e^(-x))\n",
    "    return 1 / (1 + np.exp(-x))\n",
    "\n",
    "\n",
    "def deriv_sigmoid(x: np.ndarray) -> np.ndarray:\n",
    "    # Sigmoid 求导: f'(x) = f(x) * (1 - f(x))\n",
    "    fx = sigmoid(x)\n",
    "    return fx * (1 - fx)\n",
    "\n",
    "\n",
    "def mse_loss(y_true: np.ndarray, y_pred: np.ndarray) -> np.float:\n",
    "    return ((y_true - y_pred) ** 2).mean()\n",
    "\n",
    "\n",
    "class OurNeuralNetwork:\n",
    "    \"\"\"\n",
    "    包含以下层的神经网络:\n",
    "      - 2 个输入\n",
    "      - 1 个包含 2 个神经元（h1, h2）的隐藏层\n",
    "      - 1 个包含 1 个神经元（o1）的输出层\n",
    "\n",
    "    *** 注意 ***:\n",
    "    下面代码并不是真正的神经网络代码，主要是为了演示整个过程。\n",
    "    但是可以通过这个代码去理解此特定神经网络的工作原理。\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self):\n",
    "        # 权重\n",
    "        self.w1 = np.random.normal()\n",
    "        self.w2 = np.random.normal()\n",
    "        self.w3 = np.random.normal()\n",
    "        self.w4 = np.random.normal()\n",
    "        self.w5 = np.random.normal()\n",
    "        self.w6 = np.random.normal()\n",
    "\n",
    "        # 偏移量\n",
    "        self.b1 = np.random.normal()\n",
    "        self.b2 = np.random.normal()\n",
    "        self.b3 = np.random.normal()\n",
    "\n",
    "        # 这里记录 loss，我们一会用这个绘制 loss 变化曲线\n",
    "        self.loss_history = []\n",
    "\n",
    "    def feedforward(self, x: np.ndarray) -> np.ndarray:\n",
    "        \"\"\"\n",
    "        进行前向传播\n",
    "        Args:\n",
    "            x: 前向传播的张量，包含两个元素的 numpy 数组\n",
    "        Returns:\n",
    "            前向传播的结果\n",
    "        \"\"\"\n",
    "        h1 = sigmoid(self.w1 * x[0] + self.w2 * x[1] + self.b1)\n",
    "        h2 = sigmoid(self.w3 * x[0] + self.w4 * x[1] + self.b2)\n",
    "        o1 = sigmoid(self.w5 * h1 + self.w6 * h2 + self.b3)\n",
    "        return o1\n",
    "\n",
    "    def train(self, data: np.ndarray, all_y_trues: np.ndarray):\n",
    "        \"\"\"\n",
    "        训练过程\n",
    "        Args:\n",
    "            data: （n x 2）的 numpy 数组，n 是数据的数量。\n",
    "            all_y_trues: 包含 n 个元素的 numpy 数组。\n",
    "        \"\"\"\n",
    "        learn_rate = 0.1\n",
    "        epochs = 1000  # 循环遍历整个数据集的次数\n",
    "\n",
    "        for epoch in range(epochs):\n",
    "            for x, y_true in zip(data, all_y_trues):\n",
    "                # --- 前向传播\n",
    "                sum_h1 = self.w1 * x[0] + self.w2 * x[1] + self.b1\n",
    "                h1 = sigmoid(sum_h1)\n",
    "\n",
    "                sum_h2 = self.w3 * x[0] + self.w4 * x[1] + self.b2\n",
    "                h2 = sigmoid(sum_h2)\n",
    "\n",
    "                sum_o1 = self.w5 * h1 + self.w6 * h2 + self.b3\n",
    "                o1 = sigmoid(sum_o1)\n",
    "                y_pred = o1\n",
    "\n",
    "                # --- 计算偏导。\n",
    "                # --- 命名规范: d_L_d_w1 代表 \"偏导 L / 偏导 w1\"\n",
    "                d_L_d_ypred = -2 * (y_true - y_pred)\n",
    "\n",
    "                # Neuron o1\n",
    "                d_ypred_d_w5 = h1 * deriv_sigmoid(sum_o1)\n",
    "                d_ypred_d_w6 = h2 * deriv_sigmoid(sum_o1)\n",
    "                d_ypred_d_b3 = deriv_sigmoid(sum_o1)\n",
    "\n",
    "                d_ypred_d_h1 = self.w5 * deriv_sigmoid(sum_o1)\n",
    "                d_ypred_d_h2 = self.w6 * deriv_sigmoid(sum_o1)\n",
    "\n",
    "                # Neuron h1\n",
    "                d_h1_d_w1 = x[0] * deriv_sigmoid(sum_h1)\n",
    "                d_h1_d_w2 = x[1] * deriv_sigmoid(sum_h1)\n",
    "                d_h1_d_b1 = deriv_sigmoid(sum_h1)\n",
    "\n",
    "                # Neuron h2\n",
    "                d_h2_d_w3 = x[0] * deriv_sigmoid(sum_h2)\n",
    "                d_h2_d_w4 = x[1] * deriv_sigmoid(sum_h2)\n",
    "                d_h2_d_b2 = deriv_sigmoid(sum_h2)\n",
    "\n",
    "                # --- 更新权重和偏移量\n",
    "                # Neuron h1\n",
    "                self.w1 -= learn_rate * d_L_d_ypred * d_ypred_d_h1 * d_h1_d_w1\n",
    "                self.w2 -= learn_rate * d_L_d_ypred * d_ypred_d_h1 * d_h1_d_w2\n",
    "                self.b1 -= learn_rate * d_L_d_ypred * d_ypred_d_h1 * d_h1_d_b1\n",
    "\n",
    "                # Neuron h2\n",
    "                self.w3 -= learn_rate * d_L_d_ypred * d_ypred_d_h2 * d_h2_d_w3\n",
    "                self.w4 -= learn_rate * d_L_d_ypred * d_ypred_d_h2 * d_h2_d_w4\n",
    "                self.b2 -= learn_rate * d_L_d_ypred * d_ypred_d_h2 * d_h2_d_b2\n",
    "\n",
    "                # Neuron o1\n",
    "                self.w5 -= learn_rate * d_L_d_ypred * d_ypred_d_w5\n",
    "                self.w6 -= learn_rate * d_L_d_ypred * d_ypred_d_w6\n",
    "                self.b3 -= learn_rate * d_L_d_ypred * d_ypred_d_b3\n",
    "\n",
    "            # --- 每 10 轮循环计算一次整体的损失\n",
    "            if epoch % 10 == 0:\n",
    "                # np.apply_along_axis 函数的主要功能就是对数组里的每一个元素进行变换\n",
    "                # 下面函数可以简单理解为 y_preds = [self.feedforward(i) for i in data]\n",
    "                # 这里就是对元素所有的元素进行前向传播，然后计算 loss\n",
    "                y_preds = np.apply_along_axis(self.feedforward, 1, data)\n",
    "\n",
    "                loss = mse_loss(all_y_trues, y_preds)\n",
    "                self.loss_history.append(loss)\n",
    "                print(\"Epoch %d loss: %.3f\" % (epoch, loss))\n",
    "\n",
    "\n",
    "# 定义数据集\n",
    "data = np.array([\n",
    "    [-2, -1],  # Alice\n",
    "    [25, 6],   # Bob\n",
    "    [17, 4],   # Charlie\n",
    "    [-15, -6], # Diana\n",
    "])\n",
    "all_y_trues = np.array([\n",
    "    1,  # Alice\n",
    "    0,  # Bob\n",
    "    0,  # Charlie\n",
    "    1,  # Diana\n",
    "])\n",
    "\n",
    "# 训练神经网络\n",
    "network = OurNeuralNetwork()\n",
    "network.train(data, all_y_trues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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o/l7t6+rMfAPlyvrOwtLbUgJC67qPUCZBOx1YFQSeuIjYgrLKrPN39yWUyau3ZWa9js0iWQrA3xf439pDh0XETFJfZSkwfnKtuW+asYgIFqcYXEtJabNgBSWXfj/1PpI+Re6rPj/CzefOgi8B+2Sf9GaZ+V1KIOdntYe2Bv5twDh7WRjHezPzgOxTUDnHLK4eEX8OfI/ulVSfAfbNzMvH2f9yFBGbUyY56z4EPCEzL+q1bWb+F+Uq+d/WHtoZeNOArvev3T8b+KvMPH/Adgt9n5eZ7wbuSvd51a+fyyjfhT3rp9T6uTgzP5iZ96FM5E7LUQ1tB7fYzzPpXg3btO+FYuZ715qPzsyXZ+ZVDZssUn1n/igzD6WsThtqlYRuslAX5P+APTOzXlfsJpn5A8q5Vj9ut6UESwZ5GmWFTafrKefcuzPzxh79XpeZrwRe0PDwy4b8zj8MqD/vCkoQ9dDM/GO/javj7LuZ+QLgDpTv/lEsvM6vy8xnZubv+vR1EuUzo/478dn9OvBckqTZMxgjSZIGeSXdf9y9OSKmke70ScCf19oOycxvDbuDzFwNPJkycbVgDvj78Ye3JP4mM89ss2EOSE0xYNsTgffUx9J2f8tFFVh4da05mF49pOezOL3JjcD+mfnTYXdQvY9PYHF6sVsz2/ejnh7sXlW6pl7uweJ/9/cphcQ7PWJAn/VgzM+yFA3v5TGUCe5O5wNPqT4X+srMP1DSwF1X329E1Pc7rB8w5c+eiNiHsvplp9pD76D828cK9CxjB9NdI+Z0yiqQxsB1p8w8l/JdUX/uIQPqStypdv9jPYLhg/rPzKwfa/36Ob5tUC0zr22z3ZD7/illRWqnp0fEqPUxDq7dP5/ui0EW3IHuoOuHRuwPuGnSfuDxsgw9PyJ+Mubt22P0vxp44jDHZPWcJ9KdIvOgAd8j0Pz5eWhm9gzM1/r+IHBkrTl67PfmJ0TcCnhRfXfAgaP8Ju0YxyUtz8PjMnNQgHihj1MpAfhOu0bELn0281ySpBkzGCNJkvrKzB9Sapd0uisDrrZr6ZW1+ydlZv0Py4Gqyet/qTU/ofWols63qiu4l0r9aup7z2o1xjRl5nGUVQSd9omI/SbZTzUZ+be15o9mZr3vgTLz15Q6Sp1meUzXJ76C7hz1neqBlBPpDuj0XF1TrZKrr5wZNPn24oa2l2fmNQO2u0k1QV+/WjvonpQb1isyc77ltgNFxMHAV1hc9HweeGF15fb6PDHW9H6/qNeV8k2qFVHH1Jo3Aw7ps9kWtfvTWnU0q34mob6CZRtKYHMoEbEXsGut+eg+72X9tYHl/fpMw/bcXBuo7e0eY/T/r/1WG9ZVz/3XWvPG9FlFFREPoqwM7fRTRl+t+GpKyslOB0REUx2YBS+iu5bXBzNzUK20SboROHTEbT7e0FZ/DTt5LknSjBmMkSRJw3gNpW5Lp4mmSYqInen+g/HDY+yyXmx+54i4wxj7WwofWeL+f1G7v5LulB3rqqYJjrdVqa4mpal47ySP6QdVRb6nLjN/RikY3KlfqrKuYEyVNqpzlddd+6QPvC/dqx7qwZybVIGvh9aaL6Z7Nc4wPkgJaHQatIqnyS8y85QW2w0lIg6nTIKv6mi+hrLyqil913ojInakexXUDzPz9Ba7e39DW7/3uz5RuWeLPocxq34m4T8pqaM6HTzC9vXnJqVmUi9Nk8XL+fVZH7VZPfHBhrZ6CrJOTefhB0YJuAJk5p8ox2inVXSn5+pUDyYu1FGZpW9m5q9G3OYHDW39VnZ6LknSjBmMkSRJA1V/DH6g1nwbJpt+pz6RCvCdMfZ3bkPbvcfY31IYORVGPxGxIiL2jIgXR8QHIuILEfGtiPhhUwoTulePQHcqpHVSZn4bqK86uhdw0AS7qR/TayhplNqqH9ObAHcfY3+jqq9MaVwZExGrgId0NF3FzcdSPaDSa9K7vu819K+vcR9uzq+/4PNVWrqRVHn56+l77hoR24y4q5NG7XsYETEXER8FXld76GLgoZlZD9qtjx7c0PbZNjuqAjj1c2v3PoHZen2MgyLihRMO5Db1s2dETCtF6Fiq2hmfrzU/KiJuM2jb6qKOJ9eaT65WqfXyC+APtbYjqhU2mr6zM/Pno25UrY6p1465f7USsknTef65Ufut1Fd499r/Qh2V+u/F72bmL1v23Va/77xGVSrP+mrQrZqeW/FckqQZW3Y/5CRJ0rJ1OPAsFqc0eEVEHJmZl/XYZhRNfxR/fsLzW4Nyky8nv+9XgHoU1YTYq4CnsriORxtbjz+iZeNVlKtyO/OlvzEijh2mxsgQmo7pH4xxTDfVYJjlMX0ii4NVd4yIXRqu3N2dkuppwckdQZETgZd2PLYvzUW66ytrvp+ZV/cZW71wPXTXsRjF6Sy+ajook3M9V+c0OGOM/htFxNaU1T571x46E3h0Zv5m0n0uU5N+v38I3LHj/lbALkDT5OtRlELZC4KSNumFEXEUcMKEJm2PppwrnR8Y/0ipx3IUpYbM0LWnZuAoynfMghXAX1NqF/VzAN2pkv6j3waZeWMVkOxMA7kNcEpEfJ2SqunLE/ptsly9ITMPW6K+fzTGtmcAu3Xc3wK4C3B2w3Pr5/mFmVlfoTmsH1HSfnUGfpo+RwAeQHcdlXEuDmqrvjp5WFew+Du4ZzDGc0mSZs9gjCRJGkpmXhoR7wTe0NG8JeXq7JdNoIumdEV/MYH9dhr1yvaldMkkdhIRz6VMhm05if3R/wrLdUpm/l81CdFZH2JnSq74d0+gi/oxvYp1+5huCkTsC9SDMU31YhacBKzl5r9D9omI6KxtEhGb0B3IGhQEaQpKnTVgm37ObGgbNfA1kXO4wxbAaXSvhvoWpZD2nybc33I2q/e7K6iSmSdExOeBx9ce2o2SxuidEXEBcColqHca8KNRV2ll5k8i4r10f7/uTPkefkNEXFLr5/uZecMo/UzQicAFwI4dbQczOBhTr89zFcOtfngTJZXUnWrtj6huGRH/R3ldTge+3WY1hxqN8zo2BV22q7dXK83q32+tz/HMvLo6LzvT1fb6TG+6aGUpAp/1FSvDqqcVXtX4rJt5LknSDJmmTJIkjeKfgd/X2p4fEXdsevKIZjGpvOkM+piUK8fdQUQcSkkvN6lADAz+o35d83q6ax28JiImEXRar47pzLyQ7km4pjRj9WDM1zv2cRWLc9pvT3cR6T3pTjk2KBhTry8DME5wol7sGeBWI+5j7HO4of96IOZ0YL8NLBADS/9+HwR8ss/jOwJPA95FSdH3x4g4LiIOHLHO0z9U++hlO+CJwFspKY3+FBFfjYhDIqKpMPfUVHU8jqk1/1lEPKDXNlWtuHo6x09n5rVD9Hc55bPmx712D/w58FxKfZOzI+J3EXFkRDSlRdXwrpjwtk0rbreke75q3M+5+nne6xxvam/6jJi2elBlKjyXJGm2DMZIkqShZeY1LF4ZAyV10psnsPumybUN2ci1LjpFxJ7AEQ0PXUOZRHwhJdXRrpTX/hbARpkZnbdxxrAuqAIM7601bwO8cgK7Xx+P6XpQ5GGd+f6rXPv373j8osysrzqo76MevKnfv4ru+hl1TRPP9bz5o2jadtTJ7bHO4SH3dz/Kubyhqb8XOcwEfh8jvd+ZeW1mHgTsR1ntlb2eW9kceALwaeBXEfG8YWrMZOZ8Zv4DJW3SFxl8TG0CPJKS5uu8iHj1jGvMHN3QVl/50ulgFqdhgwEpyjpVdWUeCDyf4VZr3AZ4HnBSRJweEQ8bti8tMovP1kl/pjdt3+scb7qApV+azHWe55IkzY7BGEmSNKoP0Z3H+qkR0Sv39rCua2jbtB4cGPN22JhjXJc0pdk6GtghMw/KzH/PzJMz81eZ+afMvK4zVRRARKxLK4nG8Va6r3p9WUTcbsz91o/p30/4eI7MPHrMMY7qG7X7t2Jx3v29WZwKuf586FgpU6mvrtmndv+UIVI8XdXQtllD27Catm3qY5Z+Swm8dJ6nAbwnIl6zNENaMvX3IqpC8G21er8z82uZ+TBKvZkXA58BBtX6uj1wJHBCRDTVgWrq5/TMfFy17bMpdRz6FbiHcm6+BTi1qjU0dVX9qFNqzU+tUg8uUgWjnlVr/nlmfnfEPtdk5gcy826U4ORrga8xeGXa/YBvbIDnziTM4rN10p/pTdv3Osebjp3Nx+x72fNckqTZMBgjSZJGUk2K/mOtOWhehTGKpuKgo6YFWg6WPI1XROxK+eO40xcz85AR0xmti6//yKrX5C215lvQvQpsVPVjen1YKfMtYL7Wtm+P/4fuwAuUtE2dVxnvtTApHRG3pLuo8qAUZdCcQmacCeimbdvm75+YzPx3ygR2/T14U0S8bQmGtFSW1fudmb/JzH/LzAMz8/aUuhTPAD4IXNhjs78C/m2UQWbmJZn5H5n515l5J8rV6QdQVvf1Kvb9QODYUfoZU31ly9Z019eBErjdudZ21DgdZ+aPMvPNmbkf5fP2npQaYJ+leUI5KOfOQeP0uwEaJ41n07ZNv0uuBG6stY0bVKxv3+scv7yhbX34/h6a55IkTY/BGEmSNLLM/CyL6z4A7BsRTfUjhlWvRQOLC61O26gFT3uZZUH1Xpreh8Nb7KdezHV99j5K8elOh0REvUbHKOrH9FxE3HaM/S25KnD1o1pzv2BMVyClCuie3NG0GbB79f8Pp/tvlGGCMZc2tI3z3v1ZQ1tTwHjmMvNjwIHA6tpDr4yI9w2T/mo9sKzf78w8PzM/kZnPy8wdgYdRrjCve3ZE7DZGP7/PzM9l5ssy8y7AfYFPNTz1ERHxl237GdFn6U7p1JSqrN42D3x0UoPIzBsz86eZ+f7MfDKlvs7TgXMann7EjNO5revuMsa2d21ou6TeUK3UrZ+Drc/xiNgM2KnW3Oscv7ih7Z5t+17XeS5J0mQZjJEkSW0d2tB2xBgTgU01IR7Scl9t1K/0a1v0ftdxBzIBO9buX5+ZP2yxn90HP2X9kJnXA6+rNa+gpDBra6mP6WmpB0ceHBGbVGndOifLzszM3w25j4UAYj2Yc3Fm/myIMZ3R0FZfHTaK+9fuZ48+lkRmHgc8ju5UeC8C/iMiVsx+VDM17ff7T8CvxtjfIpl5UnWF+QdrDwWllsyk+jkjM58GNKULetKk+hkwhmvoXomzb0TssHAnIrZoGM9/9/m8mMS4bsjM/6QErOqFym8PPGhafa+H7jvBba+ieVIfus/zHSJi+zH6rc9/1S8sWHA63fWZHtyy3/WO55IkjcdgjCRJaiUzTwa+XGu+N/C0lrtsSmf0xJb7aqOeJmPziLh1i/08dBKDGVN93G3TKx047kDWMR8Dflpr2z8i9mi5v6U+ppvqrExikr4eSNkE2IvhUpT12se+tf8uaKo50+QM4Ppa2+PbBCWqyb69as0/z8wlT1PWKTO/CjyK7roHBwP/GRFLnjJxik5raDugzY4i4r6Umi+dvlevoTUh/0h3irlpXHF/BN2plmZ5ZX89VdlGwDM77h9ISQXZaawUZcPKzKtpTkG5wa58aOHuETHy6phqm/pKsNMzs56ObMHEznPgyUPun8y8iu5A0IOqFLCqeC5JUjsGYyRJ0jheRXdO7zcBQxUl7pSZZ9Kd8/4BEfHwlmMb1c8b2h44yg6qP9RnNd5+rqndv2VEjPS7LyIeynhXv65zqgmhVzc89PaWuzwZuKLW9qSIuHPL/Y2qqTjxJIoQnwZcW2vblyFSlC2oVrt0poK5X0Tck+6VZcOkKCMz11Dq2XS6Dc21KgZ5LlBPs9KUYmrJZeYpwD50B1yfDBzfVDh9fZCZFwBn1ZrvVwVWRvXChrapvN+ZeTndKdbGqb/Rq595ur9PJ95Pn/6/09D/szr+/+DaY5cBX5jmmGrObmib2euznnjOhLb5Sp/nf7Wh7bktfs9sRUmr1anpO6PT8fXdAK8Ypd8NhOeSJI3IYIwkSWotM39KWU3Q6Y7AC1ru8s0NbR+OiFkUkm9KV1H/432QI1gev6/qqV42pftK/54i4hZ0p9PZIGTml1hczwRKepL9W+zrGuDdteYVwMcjYuN2IxxJU5HzsesAZeYNwKm15n0pQYEF9bowTToDLStoPv+HCsZUmoqhv7M6nn8rHwsAACAASURBVIcSEXegBJk7JaWm0LKUmadTiqHX6xw8BvhyREwiALccNb3fI9XMiYgH0B0YuIYprdKogmP1QuBN9W8moV6falr99FJ/De8SEQ+ugtF71h77RBVQnZWm2l2zfn3WdS8ZZaVI9dyX1JpvAI7utU1m/gCop1i9J/D8YfutvBmo/448NjO7atV0+He609c+JyIeOWLf6zvPJUka0XKYLJAkSeu211P+oO7UNo3Ex+m+yu6OlAnF27XZYURsGRGHRsQz+j0vM39F95W8T42Iei2BXv28ltmmoOrn2w1tbx4mbVFEbAp8hvEK9K7rXtnQ1vaYfjfdRYIfAHy2ulp3ZBGxXUS8KSIeMeCpTbVWHt2mzwb19GH3ATrP0e9VqV76qQdaHlu7f05mXjjCmL5M9+fHzsAnhykoHBG3BE6gO33SFzOz/tmwrFSB8YcAF9QeehjwtYjYevajmrpj6F4R9CDgX4fZuAq8fZbuv4k/kpn1tJUL2+waEa+LiG1HHWzleUA9EPs/Df1sHhHviIh6wfGhRMT+wB0G9TNlx9Cdku0QuoNfMGLwKyIeFxGHjBHUfllDW9/XJyKOjois3Q5r2f/6YGPguOpzs6/qOcfRfex/MjPr349172poe2dEDFV/LSL+H92r35LuCyUWPyHzCuBf6rsDPh0Rew/Td20c241yYcCsLMW5JEkbOoMxkiRpLJl5PkNOfg2xr3lKep36JO4DgR9HxAuHSbsTESsjYt+I+CBwPmXFym2GGMKHa/c3Ar4UET0Lt0bEbSPiGOCNVVO9bsVS+AHwm1rbgykTJz3r4FSBp1O4ecK+flXoBiEzv0+ZpJ3Evq4Enkp3/ZbHAj+KiIOGDBRsEhH7R8THKe/taxiQCqSqcVIPThwSEX/XNhDUYdCKlWFWtExiHzepanw8m+4J4P0pAYmeV3FHxAMpq33+ovbQn+i+mntZqgJGewK/rD20O/DNMQIIy1JVr6BpFeaLIuIz/Qp9R8SjKe/3jrWHzqNcYNDL5sDhwPkR8fGIeEIVwO4rIuYi4uXAO2oPzQOfathkJfBy4NcRcXz1OTHwnI2IjSLiYMqFDXVNbVOTmRfRne7tQBanKwM4IzNHnby9E6UuzfkR8a6I2HOY1FURcevq+7qevvCcaoWZhrPwO+cewKnVCrNG1e+Kb1fP7XQpzRc+LFIViq/XJ9yUcpHOi3u979V35luBD1GCKJ3ek5lNq6Hr3gh8r9a2NfDViHjbkIGoB0TE+ymfLdsN0eeseS5J0owN/MNPkiRpCG8B/obyR+pYMvNnEXEQ5SrKzt8q21HS0rw5Ik4Gvg9cQqnJsVnV906UOif3BrZs0f2RwItZPEG3LWWy4WuUguQXUlIq3ZaS+usRlIkBKBMUr2bAFZfTlpnzEfEGuosoPxY4LyI+R5lguIwyubgT8JeUFRudkxYvBj46/REvS/9ImWQY+/dyZn4jIl4KvL/20C6UCdJ/joiTKKnyLgWuBragHNN3ohzT9+Lm42wU/8HimjcrKFca/3NEXEg5f+rBiyMz88gB+/0xpUD4Nj0eHxhIyczfRsTZwN3a7qNhn6dVx/7htYceBpwZEd8Avgn8lvJa7EgJPu5B94RdAs+rAs7rhMw8PyL2onxW/XnHQ/cGTo6IfatJ8lk6PCL+dgL7eUpmLqrtlZnHRsR+wP+rPfcA4DER8RXKRPDFwCaUlZaPo5xPdWuAZ1RXxA+yCXBQdbsuIn5COSd+QQngXUVZBXAbSoDvUTRPxL6tqn/TywrK59DjgTUR8b9VP2dT0hBeAayq9r1b1U/TappPZOZ3h/h3TdpRlO+WBVtUt/pz2toO+LvqdnlEnAH8hHIRxp+A6ygr3e4A3J/F39cLkvJdt655fkS0qYnV5NEjfi68Hfh7yu+HPwO+FxGnUuq/LBzPO1KOx71o/mx9QWYOm87qEMr72pkSazPKhUCviIjjKDWk/gTcmnJ+P6H6/7ozaK4N1yUz10TEkykXuHT2PUcJJL20+j268BlzJeX43r4awx50B3yXqw35XJKkmTIYI0mSxpaZf6yuQDxiQvv7YkTsA3ya7hUtW1OudB+5hscQ/V4ZEYdQJhTqKb0eWd16WUtZATHMRN7UZeZRVRqrp9Ue2gx4ZnXr582Z+bGI2CCDMZn5i4j4EO3rH9X39+8RcQll4rE+Gbk98JTqNmn/RrkSfbdae1AmiZomigauIsvMjIhvUlay1V1JCZYO40SagzHz9C+u3G9sb4yIAN5Qe2gVZXLwUUPsZg1wSGYe22YMSykzL67S6Pw3cL+Oh+4OfDsi9snM82Y4pF7H2ah6BSOfSzle6sXBN6WkjhwmfeSVwBOrwvNtxrV7dRvFp+k+RvtZRQnM3nfEfk6iO03TrJxA/6DtDcAnJ9TXNpQJ4kHpGzsl8PeZ+fUJjWGWtq9ukzA34vPPpQQij6MEDIMSdBmmNl0Cz8/Mzw3bWWZeUgWZ/xuor3DcCRg22Hsq8Liq7tmwfV9YrZz8It0rJzdl+O+UdcmGdi5J0kyZpkySJE3KeymrRiYiM0+h1KH4ON1X7o+0K8qkblMdlaZ+v0EJ9FwzQh+XAPtl5gmjD2+qDgE+MuI21wMvyczXTmE865o3MNpx0Fc1+XQ/4L/G3NXaah//O0Sf1wL7UVZKTFqvlSsnZ2Y9Lduo+/hRr7odw8jMwymByDarQM4EHpGZn2jb/1LLzMuBfej+3LsTJSBz19mPajoycz4zn0upXfDHFrv4HrBn9dk/yLV0p9Ec1dWUK/Of1qdo/VpKEGMcayir4B5VpUucucxcTf9gywlVOsVRXU536sdR/QbYPzPfM+Z+NkiZ+QXKiq1RPqf/AByUmR9s0d+vKCtNjqX8rhvFGsoqmkdk5sifEdXqtQdX+xg6kFNzJcsjjW2d55IkzZjBGEmSNBGZeT39c+232efvMvOvgTsD76GkoRjGVZTJ6r8F7piZD6/qgAzb71coBew/TJk46+UPwDuBu2fmN4fd/6xk5g2Z+TfAY4BBV3xfAXwA2C0z3zf1wa0DMvP3wD9PeJ/nZOZfUa6w/RDlCuNhXE6pY/M84PaZ+VeZec6Qff42Mx9JuaL+bcBXKfnr/8R4kzC9AimjpBf7Vo8xjJyirC4zP0W5ivpQSmqnfhN4aylXTf8NcM/MPHnc/pdaNQH/KLrrduwAnBIR9au812mZ+V5K6r830V0rqe56SoDySZm5e2b+dMg+zqGkPnokJdDxfWD1kEM8C/gn4M6Z+baqxlGvfq6mrHrYi5IG9BRKIGgYv6Gkkbp7Zv7DKKsApqRfGrJWKcoy82OUFKJPo1xwcBbDTdDfSHktnw/cLTO/2KZ/FZn5X5Q0Zf9G/xpzlwLvo7zm/zlGf5dm5lMoKVU/zeDg6+8o37N3z8yXVr9T2/Z9TWa+lPJ79N2UlISDXEtZaf0c4HaZeXHb/qfFc0mSZi/6/AaUJEladqqizPehTIhtQ8lZfg1lIuC3lEm43/Sb6Bqxv1WUqzF3qfpcQam18lPghyOsAFhy1Wv3YOB2lHRvNwC/p/zx/eN16d+yPomInYB7cvMxvQklCHglJV/72Zn526Ub4fqhOv7vT8mNvy1lxd2llFz/3xuyVojWERFxB0rdhm0p59ZqyirGiyjv97DBjUH9bEwJ+u1C+WzdglIv5lpKkPs84H8y87Ix+1lZ9bELJaC2JSVN0sJqnfOBn26onxURsRVlovxOlHN8c8r39VWUwPM5lNdnYqsdNwQRsTPdFw0ckplH1563ivL5uhvle+xGSjDkXOC7mTnOCudeY1tBWW26M+U835LyXl9CKSQ/cPXomP3fgfLdvfAZs3C8/R74OeW7e9hg7bLhuSRJ02UwRpIkSZIkSYsMG4yRJEnDMU2ZJEmSJEmSJEnSFBmMkSRJkiRJkiRJmiKDMZIkSZIkSZIkSVNkMEaSJEmSJEmSJGmKDMZIkiRJkiRJkiRNkcEYSZIkSZIkSZKkKTIYI0mSJEmSJEmSNEUGYyRJkiRJkiRJkqYoMnOpxyBJkiRJkiRJkrTecmWMJEmSJEmSJEnSFBmMkSRJkiRJkiRJmiKDMZIkSZIkSZIkSVNkMEaSJEmSJEmSJGmKDMZIkiRJkiRJkiRNkcEYSZIkSZIkSZKkKTIYI0mSJEmSJEmSNEUGYyRJkiRJkiRJkqbIYIwkSZIkSZIkSdIUGYyRJEmSJEmSJEmaIoMxkiRJkiRJkiRJU7RyqQcgresiYivgoR1NFwCrl2g4kiRJkiRJkqRmc8COHfdPzswrZtGxwRhpfA8FTljqQUiSJEmSJEmSRrI/8IVZdGSaMkmSJEmSJEmSpCkyGCNJkiRJkiRJkjRFpimTxndB553Pf/7z7Lrrrks1FkmSJEmSJElSg1/+8pc8/vGP72y6oNdzJ81gjDS+1Z13dt11V3bbbbelGoskSZIkSZIkaTirBz9lMkxTJkmSJEmSJEmSNEUGYyRJkiRJkiRJkqbIYIwkSZIkSZIkSdIUGYyRJEmSJEmSJEmaIoMxkiRJkiRJkiRJU2QwRpIkSZIkSZIkaYoMxkiSJEmSJEmSJE2RwRhJkiRJkiRJkqQpMhgjSZIkSZIkSZI0RQZjJEmSJEmSJEmSpshgjCRJkiRJkiRJ0hQZjJEkSZIkSZIkSZoigzGSJEmSJEmSJElTZDBGkiRJkiRJkiRpigzGSJIkSZIkSZIkTZHBGEmSJEmSJEmSpCkyGCNJkiRJkiRJkjRFBmMkSZIkSZIkSZKmyGCMJEmSJEmSJEnSFBmMkSRJkiRJkiRJmiKDMZIkSZIkSZIkSVNkMEaSJEmSJEmSJGmKDMZIkiRJkiRJkiRNkcEYSZIkSZIkSZKkKTIYI0mSJEmSJEmSNEUGYyRJkiRJkiRJkqbIYIwkSZIkSZIkSdIUGYyRJEmSJEmSJEmaIoMxkiRJkiRJkiRJU2QwRpIkSZIkSZIkaYoMxkiSJEmSJEmSJE2RwRhJkiRJkiRJkqQpMhgjSZIkSZIkSZI0RQZjJEmSJEmSJEmSpmjlUg9A0rrnvMuu4fVf+D/WrL2R1fM3smb+RlavLf/9wov3ZLON/WiRJEmSJEmSpAXOmEoa2XVr5jnlnEsbH1u99kY223jGA5IkSZIkSZKkZcw0ZZJGtmpF74+ONfM3znAkkiRJkiRJkrT8GYyRNLK5PsGY1QZjJEmSJEmSJGkRgzGSRrZqZfR8bM18znAkkiRJkiRJkrT8GYyRNLK+K2PWujJGkiRJkiRJkjoZjJE0slUrrRkjSZIkSZIkScMyGCNpZNaMkSRJkiRJkqThGYyRNLJVfYIxa0xTJkmSJEmSJEmLGIyRNLIVGwUbRfNja+ZztoORJEmSJEmSpGXOYIykVnqtjrFmjCRJkiRJkiQtZjBGUitzK5s/Pm4wTZkkSZIkSZIkLWIwRlIrc66MkSRJkiRJkqShGIyR1IppyiRJkiRJkiRpOAZjJLWyamU0thuMkSRJkiRJkqTFDMZIaqXXypjV8znjkUiSJEmSJEnS8mYwRlIrPWvGrHVljCRJkiRJkiR1MhgjqZW5lb1WxhiMkSRJkiRJkqROBmMktdIrTZkrYyRJkiRJkiRpMYMxklpZtSIa29e4MkaSJEmSJEmSFjEYI6mVXitjVs/njEciSZIkSZIkScubwRhJrcz1SlPmyhhJkiRJkiRJWsRgjKRWeq6MsWaMJEmSJEmSJC1iMEZSK3MrXRkjSZIkSZIkScMwGCOpld41YwzGSJIkSZIkSVIngzGSWplbGY3ta+ZzxiORJEmSJEmSpOXNYIykVnqtjFljzRhJkiRJkiRJWsRgjKRWegZjTFMmSZIkSZIkSYsYjJHUijVjJEmSJEmSJGk4BmMktTK3skcwxjRlkiRJkiRJkrSIwRhJrcytiMZ205RJkiRJkiRJ0mIGYyS10rtmTM54JJIkSZIkSZK0vBmMkdRK72CMK2MkSZIkSZIkqZPBGEmtrOpVM8ZgjCRJkiRJkiQtYjBGUiu9asasXmswRpIkSZIkSZI6GYyR1Mpcj5UxpimTJEmSJEmSpMUMxkhqpXfNmJzxSCRJkiRJkiRpeTMYI6mVnsEY05RJkiRJkiRJ0iIGYyS1MtcjGLPaNGWSJEmSJEmStIjBGEmt9E5TZjBGkiRJkiRJkjoZjJHUytzK5o+PGxPWGpCRJEmSJEmSpJsYjJHUyqoV0fOxNfM5w5FIkiRJkiRJ0vJmMEZSK73SlIF1YyRJkiRJkiSpk8EYSa30SlMG1o2RJEmSJEmSpE4GYyS10m9ljMEYSZIkSZIkSbqZwRhJrfStGbPWmjGSJEmSJEmStMBgjKRW+qUpWz0/P8ORSJIkSZIkSdLyZjBGUitzfdKUrXZljCRJkiRJkiTdxGCMpFasGSNJkiRJkiRJwzEYI6kVgzGSJEmSJEmSNByDMZJaWbUiej622mCMJEmSJEmSJN3EYIykViKiZ0Bmzbw1YyRJkiRJkiRpgcEYSa3N9UhVtnqtK2MkSZIkSZIkaYHBGEmtrVrZ/BFizRhJkiRJkiRJupnBGEmtreqxMsZgjCRJkiRJkiTdzGCMpNZMUyZJkiRJkiRJgxmMkdTaqhXR2L5mPmc8EkmSJEmSJElavgzGSGrNNGWSJEmSJEmSNJjBGEmtza00TZkkSZIkSZIkDWIwRlJrvVbGrHZljCRJkiRJkiTdxGCMpNbmTFMmSZIkSZIkSQMZjJHU2qqV0dhuMEaSJEmSJEmSbmYwRlJrvdKUrZnPGY9EkiRJkiRJkpYvgzGSWutZM2atK2MkSZIkSZIkaYHBGEmtza3sEYwxTZkkSZIkSZIk3cRgjKTW5nqlKXNljCRJkiRJkiTdxGCMpNZWrYjG9jWujJEkSZIkSZKkmxiMkdRar5oxa+ZzxiORJEmSJEmSpOXLYIyk1noFY6wZI0mSJEmSJEk3MxgjqbWNV/YIxlgzRpIkSZIkSZJuYjBGUmu905QZjJEkSZIkSZKkBQZjJLVmMEaSJEmSJEmSBjMYI6m1VSujsX31fM54JJIkSZIkSZK0fBmMkdTaXK+VMdaMkSRJkiRJkqSbGIyR1JppyiRJkiRJkiRpMIMxklqbW9n8EbLaYIwkSZIkSZIk3cRgjKTWeq6MMU2ZJEmSJEmSJN3EYIyk1latiMb21fM545FIkiRJkiRJ0vJlMEZSa3PWjJEkSZIkSZKkgQzGSGqtZ5oygzGSJEmSJEmSdBODMZJaW7XSYIwkSZIkSZIkDWIwRlJrvdOUJTfeaN0YSZIkSZIkSQKDMZLGMLcyej625kZXx0iSJEmSJEkSGIyRNIZeNWOgrI6RJEmSJEmSJBmMkTSGvsGYta6MkSRJkiRJkiQwGCNpDP1XxhiMkSRJkiRJkiQwGCNpDHN9gjGrDcZIkiRJkiRJEmAwRtIY5lb2CcaYpkySJEmSJEmSAIMxksawakX0fGzNfM5wJJIkSZIkSZK0fBmMkdTaqj4rY6wZI0mSJEmSJEmFwRhJrVkzRpIkSZIkSZIGMxgjqbVVfYIxa6wZI0mSJEmSJEmAwRhJY1ixUbBRj7Ix1oyRJEmSJEmSpMJgjKSxzPWoG7N6fn7GI5EkSZIkSZKk5clgjKSx9EpVtnqtK2MkSZIkSZIkCQzGSBrTXI9gzJp5a8ZIkiRJkiRJEhiMkTSmXitjDMZIkiRJkiRJUmEwRtJYVq2MxnaDMZIkSZIkSZJUGIyRNJaeNWPmrRkjSZIkSZIkSWAwRtKYetWMWb3WlTGSJEmSJEmSBAZjJI1pbqU1YyRJkiRJkiSpH4MxksbSK03ZGlfGSJIkSZIkSRJgMEbSmFatiMZ2V8ZIkiRJkiRJUmEwRtJYeq2MWT2fMx6JJEmSJEmSJC1PBmMkjWXjHjVjVpumTJIkSZIkSZIAgzGSxtSzZoxpyiRJkiRJkiQJgJVLPQAtFhG7AA8AdgDmgD8CZwOnZeb1SzCeVcBdgd2A7YEtgKuBy4H/BX6WmROddY+I2wO7A3cANgWuBM4BTs3MqyfZl8ZnMEaSJEmSJEmS+jMYs0xExOOB1wH36fGUqyPiaOANmXnZlMdyR+AA4BHAnpSASC9XRMTHgX/JzF+M2e9DgcOAvXs8ZXVEfBp4fWaeN05fmpzeNWMMxkiSJEmSJEkSmKZsyUXExlUw43h6B2IANgdeDJwZEQ+Z4li+B/waeDslGNMvEAOwFfAi4GcR8fKIiBb9RkS8HTiJ3oEYKCuF/rrq60mj9qPpmFvZ/Javmc8Zj0SSJEmSJEmSlieDMUsoIjYCPg0cVHtoHjgX+AlwRe2xbYGvRMTuUxjSKuCBPR67vhrT6cCZwOra43PAO4D3tej3vcAram0JXACcAdRXAm0GfDointCiL01YzzRla10ZI0mSJEmSJElgMGapvQLYv9Z2JLBTZt4pM+8N3Ap4InB+x3NuARwbEVtNeXznUtKGPRjYshrTAzJzN2BryiqV39S2eWFEvHjYDiLiQMqKn06fA+6amTtl5n0zc1tgX0qNmgUrgGMiYucR/j2agjnTlEmSJEmSJElSXwZjlkhEbAO8ptb86sx8QWZetNCQmTdm5vHAHsB5Hc/dAfj7KQ3vO8B+wC6Z+YbMPC0z13Q+ITOvy8yPA/emrJbp9MaIuNWgTiJiDjii1nwk8OR6/ZnM/AbwEOCHHc1bAG8Y5h+k6Vm1ssfKGIMxkiRJkiRJkgQYjFlKh1KCCQtOoTswcZPM/C3wN7Xmv6uCOpOyGnhsZu6ZmV/LzIFFPzLzj8DjgWs6mrcGhqnp8mxg5477vwD+rle/mXkF8CwWp0g7KCLuNkRfmpJeacpWm6ZMkiRJkiRJkgCDMUuiqhVzSK35sEHBj2p1yLc7mrYADpzUuDJzdWZ+qcV2FwHH1Jr3G2LTenDprZl5/YC+zqTU2Vmwgu7XUjM0tyIa210ZI0mSJEmSJEmFwZilsQewbcf9XwMnDbntR2r3Hz+JAU3At2v3d+r35IjYAbhPR9PVwLFD9lV/Dep1dzRDvVbGrJkfuLBKkiRJkiRJkjYIBmOWxmNq978+TEqwhefW7u8dEZtNYEzj+mPt/lYDnl9/Db6Tmdc0PrPbd4BrO+7fNSLuPOS2mrDewRhXxkiSJEmSJEkSGIxZKveq3T9t2A2rlGDndTTNAX82gTGN6/a1+5cPeP44r8Fa4AcD9qcZmVtpzRhJkiRJkiRJ6sdgzNK4e+3+mSNuX39+fX9LYa/a/XMGPH99fA02SHM9VsasdmWMJEmSJEmSJAEGY2YuIjalu57KBSPupv78u7Yf0fgiYkvggFrzlwdsVh/zOv0abMhWrYzGdtOUSZIkSZIkSVKxcqkHsAG6NdA5e70GuGTEffy2dn+7sUY0vtcCm3fcvwz4rwHbbFu7f+GIfU7lNYiI7ege2yC7TKLvdVXvmjHDlkGSJEmSJEmSpPWbwZjZ27x2/9rMHHXWul7ovr7PmYmIPYC/rzW/KTOv7bPNpsCKWnP93zTItF6DFwL/NKF9bRB6BmOsGSNJkiRJkiRJgGnKlkI9aHB9i31cN2CfM1GtIvkUiwMrpwPvG7Bp03hHfR2WxWsga8ZIkiRJkiRJ0iAGY2Zvk9r91S32cUPt/qYtx9JaRGwMHA/s2NF8FfD0zJwfsHn9NYDRX4clfw1UzK3sHYwZfdGXJEmSJEmSJK1/TFM2e/UVIHMt9rHxgH1OVURsBHwc2KOjeR44KDN/OcQumsY716O9l2m9Bu8HPjPiNrsAJ0yo/3VOrzRlmTB/Y7JyRTQ+LkmSJEmSJEkbCoMxs3d17X7TKpFB6qtA6vuctvcDB3TcT+A5mfnFIbdvGu8mjBZQmcprkJmXAJeMsk3Ehh1sWNUn2LJmPllZrw4kSZIkSZIkSRsY05TNXj1ocIsYfTZ/swH7nJqIeCvwvFrzP2TmUcPuIzOvo6yk6VT/Nw2yZK+BFuu1MgasGyNJkiRJkiRJYDBmKVxGWUmyYBWw3Yj7uH3t/kgrOdqKiFcBr6o1H56Z726xu0tr93cYcfsleQ3UrVfNGIA1BmMkSZIkSZIkyWDMrP1/9u41yra0rA/9/9211uqGbhSERqCxaS5y04MoHDhpRToBUQwBBjYQlXOOBDWiaIacACEeJBiCttFEzAh6jmLQjBiFMAwK3pA0CE04otiaASIQ5dqN0MitW7rrsp/zoWpbq9des2rXrrlW1ar5+41Ro9Z815xvPXvRoz7Un+d9drpCPjyzfNkBt5m9/73nX9G5aa19X5IfnVl+eVW9+Dy3/POZ62P/GTDfXp0xwhgAAAAAAGHMUZkNDh58wOcftM9+vWqt/R9J/t3M8i8k+cFDbLtSnwHd9uqMWd8UxgAAAAAACGOOxnUz11ec64OttbsnuXxqaSPJe3qoqevnfUu2g5fpuTavTvJdVVXznzonh/kMRkkesc9+LMl4rXvkkc4YAAAAAABhzFF5/cz1Y1tr3X/Rvq3HzVxfU1ULGV7fWnt8kl9Osja1/IYkz6iqw/6V/Q0z11e01i46x2e/Nsntp67fV1XvO2Q9nKfJHseUrW8eJq8DAAAAADgZhDFH4+1Jbpy6vk+SK8/x2WfNXL+uj4JmtdYeneS1SSZTy9ckuaqqNg67f1V9JMkfTy1dnORp5/j4Uj4Dzo2ZMQAAAAAAexPGHIGdrpJXzSy/eL/umNbaY5I8amrp89k+MqxXrbWHJ/mNJLebWn5HkidW1S09/qhXzlz/s9bahfvU9qAkT59amvdZskTCGAAAAACAvQljjs7VSaaPF3t0khd03dxauzTJz88sv7yqbpx3/9RzNfN15T73f0WS305yh6nl65I8fgHHof1ckg9PXd8/yb/tCqVaa1+U5Jdy226dX66qhc3MYX97zYxZF8YAAAAAAGR01AUMVVXd2Fp7WZKXTS3/aGvtsiQvrarrk6S1dirJE5O8PMllU/den+Qn+6yptXb3JL+b5M5TvRg0zQAAIABJREFUyzcn+fEkDz/3sTbbqur39nl/vbX2z7I9l+aM70lyl9baP6+q90/V9veS/NskD5m696YkP3ygouhday2TtVNzg5f1TWEMAAAAAIAw5mhdneSKJE+YWnt2ku9urX0oyWeT3DvJHWee+0KSp1XVZ3qu5wFJ7jGzdlFuG5YcxL7pTVX959bao7L97z7jqiTf0lr7SJJPJrlXkrvMPHo6yTOr6i/PszZ6NF5rWd86e31jq5ZfDAAAAADAMeOYsiO0MzvmqUl+ZeattST3SfLVOTuI+VSSb66qaxdf4dI8J9tdL9NatjuBHpazg5i/SfKtVfVfllAb52A8mv+rxMwYAAAAAABhzJGrqluq6luz3Q1y3R633pzkFUkeXFVvXkZty1JVp6vquUn+XpK37nHrepL/lOQrq+rVSymOczJeE8YAAAAAAHRxTNkxUVWvTfLa1tr9kjwyyaXZHlT/mSR/luTaqrrlPPY950EvOyHPwQbD9KiqrklyTWvtntk+vu2yJBcm+XyS9yd5W1V97qjqo9ukI4wxMwYAAAAAQBhz7FTVB5J84KjrOEpV9dEkOl9WyKTjmLJ1nTEAAAAAAI4pAw5vvDa/oWpDZwwAAAAAgDAGOLzumTG15EoAAAAAAI4fYQxwaF1hjGPKAAAAAACEMUAPJp2dMcIYAAAAAABhDHBo41HHzBhhDAAAAACAMAY4vK7OmPVNYQwAAAAAgDAGOLSumTEbW7XkSgAAAAAAjh9hDHBo41FHZ4xjygAAAAAAhDHA4XUdU7bhmDIAAAAAAGEMcHjjtTZ3fUNnDAAAAACAMAY4PDNjAAAAAAC6CWOAQ5t0zIy51TFlAAAAAADCGODwOmfGOKYMAAAAAEAYAxxe9zFlwhgAAAAAAGEMcGjCGAAAAACAbsIY4NDGozZ3fX2rllwJAAAAAMDxI4wBDq1zZsymzhgAAAAAAGEMcGiT0fxfJeuOKQMAAAAAEMYAh2dmDAAAAABAN2EMcGidYYxjygAAAAAAhDHA4Y3X2tz19a1aciUAAAAAAMePMAY4tIljygAAAAAAOgljgEMzMwYAAAAAoJswBji0yWj+r5J1M2MAAAAAAIQxwOF1dcZsnq6cPm1uDAAAAAAwbMIY4NAmo9b53sZp3TEAAAAAwLAJY4BD6+qMSZKNLZ0xAAAAAMCwCWOAQ9szjDE3BgAAAAAYOGEMcGiT0V6dMcIYAAAAAGDYhDHAoU326Iy5VWcMAAAAADBwwhjg0PaeGSOMAQAAAACGTRgDHNp4rXW+t7FVS6wEAAAAAOD4EcYAhzY2MwYAAAAAoJMwBji0vWbGrAtjAAAAAICBE8YAh7bnzJhNYQwAAAAAMGzCGODQ1k61rJ2aPzdGZwwAAAAAMHTCGKAX47X5YYyZMQAAAADA0AljgF50HVW2vllLrgQAAAAA4HgRxgC9mHSEMTpjAAAAAIChE8YAvejqjBHGAAAAAABDJ4wBejEZdR1TJowBAAAAAIZNGAP0YrzW5q7rjAEAAAAAhk4YA/Si65iy9a1aciUAAAAAAMeLMAboRdcxZTpjAAAAAIChE8YAvejqjNkwMwYAAAAAGDhhDNALM2MAAAAAAOYTxgC9mIzW5q7fKowBAAAAAAZOGAP0YtLVGbNZS64EAAAAAOB4EcYAveicGaMzBgAAAAAYOGEM0AthDAAAAADAfMIYoBddYcy6MAYAAAAAGDhhDNCLyahjZsyWmTEAAAAAwLAJY4BeTLo6Yza3llwJAAAAAMDxIowBetE9M0ZnDAAAAAAwbMIYoBfjUVcYY2YMAAAAADBswhigF12dMeubwhgAAAAAYNiEMUAvJmtt7rrOGAAAAABg6IQxQC8mnceUmRkDAAAAAAybMAbohWPKAAAAAADmE8YAvegKYxxTBgAAAAAMnTAG6MWkqzNGGAMAAAAADJwwBujFeNTmruuMAQAAAACGThgD9KL7mLJaciUAAAAAAMeLMAboRdcxZRubOmMAAAAAgGETxgC9GI/m/zq51TFlAAAAAMDACWOAXnR2xmydTpWjygAAAACA4RLGAL3omhlTlWydFsYAAAAAAMMljAF6MV5rne9tbAljAAAAAIDhEsYAvejqjEmSdXNjAAAAAIABE8YAvbhg1P3rZEMYAwAAAAAMmDAG6MWenTGbwhgAAAAAYLiEMUAvxjpjAAAAAADmEsYAvRivtc73hDEAAAAAwJAJY4BeTPY8pqyWWAkAAAAAwPEijAF6sdfMGJ0xAAAAAMCQCWOAXkzMjAEAAAAAmEsYA/RidKp7Zsz6pjAGAAAAABguYQzQi9Za59yYdZ0xAAAAAMCACWOA3ozX5nfHbGzVkisBAAAAADg+hDFAb8Ydc2PMjAEAAAAAhkwYA/Rm3HFMmTAGAAAAABgyYQzQm66ZMbduCmMAAAAAgOESxgC9mTimDAAAAADgLMIYoDfjtTZ3fUNnDAAAAAAwYMIYoDfdM2NqyZUAAAAAABwfwhigN11hzLpjygAAAACAARPGAL0xMwYAAAAA4GzCGKA3k67OGDNjAAAAAIABE8YAvRmvtbnrOmMAAAAAgCETxgC96ZoZs7FVS64EAAAAAOD4EMYAvRl3zIxZ1xkDAAAAAAyYMAboTdfMmA0zYwAAAACAARPGAL3pDGN0xgAAAAAAAyaMAXozHrW5644pAwAAAACGTBgD9Gbc0RmzvllLrgQAAAAA4PgQxgC9cUwZAAAAAMDZhDFAb7o6Y4QxAAAAAMCQCWOA3ghjAAAAAADOJowBejMZdcyM2TIzBgAAAAAYLmEM0JvxWpu7vr6pMwYAAAAAGC5hDNCbrs4Yx5QBAAAAAEMmjAF6Y2YMAAAAAMDZhDFAbzrDGMeUAQAAAAADJowBetM5M2arllwJAAAAAMDxIYwBenOBmTEAAAAAAGcRxgC96TqmbN0xZQAAAADAgAljgN50zozRGQMAAAAADJgwBuhNVxizebpy+rS5MQAAAADAMAljgN5MRq3zvY3TumMAAAAAgGESxgC96eqMSZKNLZ0xAAAAAMAwCWOA3kxGe4QxmzpjAAAAAIBhEsYAvdmrM2Z9SxgDAAAAAAyTMAbozWSvMEZnDAAAAAAwUMIYoDd7z4wRxgAAAAAAwySMAXozXmud721s1RIrAQAAAAA4PoQxQG/GI50xAAAAAACzhDFAb/acGSOMAQAAAAAGShgD9GavmTHrm8IYAAAAAGCYhDFAb9ZOtaydmj83xjFlAAAAAMBQCWOAXo3XhDEAAAAAANOEMUCvuo4qW9+sJVcCAAAAAHA8CGOAXl0wmv9rRWcMAAAAADBUwhigV92dMcIYAAAAAGCYhDFAr7rCGJ0xAAAAAMBQCWOAXo3X2tx1YQwAAAAAMFTCGKBXnceUbdWSKwEAAAAAOB6EMUCvJiPHlAEAAAAATBPGAL2adM2M2RTGAAAAAADDJIwBetV9TJkwBgAAAAAYJmEM0KtxxzFlwhgAAAAAYKiEMUCvJmtt7vrGZi25EgAAAACA40EYA/Sq65iyDZ0xAAAAAMBACWOAXgljAAAAAABuSxgD9GpiZgwAAAAAwG0IY4BedXXGrG8KYwAAAACAYRLGAL2arLW5644pAwAAAACGShgD9Kp7ZkwtuRIAAAAAgONBGAP0atwxM0ZnDAAAAAAwVMIYoFdmxgAAAAAA3JYwBujVBTpjAAAAAABuQxgD9Gq81uaurwtjAAAAAICBEsYAveo6pmxjs5ZcCQAAAADA8SCMAXrVGcbojAEAAAAABkoYA/Rq0hHGOKYMAAAAABiq0VEXwK7W2n2TPCLJPZNMknw6yXuTvL2qbjnK2uBcjUfzZ8bojAEAAAAAhkoYcwy01p6c5EVJvqbjlptaa69K8pKqunFJNbUkD8x2OPSIJI9M8pAk46nbfrGqvuM8978yyTWHKPFDVXX5IZ5nQSZra3PXN7bMjAEAAAAAhkkYc4RaaxckeWWSb9/n1ouTPCfJ01trV1XV7y+wpmfu1PPwJF+8qJ/DyTVem98Zs76pMwYAAAAAGCYzY45Ia+1Ukl/N2UHMVpK/THJdks/OvHdJkt9qrf2dBZb2pCSPiSCG8zQemRkDAAAAADBNZ8zReV62g49pP5vkX1bV9cnfBjZPSvJTSS7buef2SV7dWvvKqpoNaxbt5iQXLWjv/5jklw5w/xcWVAeHNFmbH8ZsbJ1OVWX7BDwAAAAAgOEQxhyB1tqdk/zQzPILq+rHpheq6nSSX2ut/UGStyW5fOeteyZ5bpIXL7DMjyd5Z5I/2Pn+ziQ/sMCf+RdV9XsL2pslGneEMVXJ1unKqOMYMwAAAACAk0oYczSen+QOU9e/n+Tqrpur6mOtte9MMh1W/GBr7aer6lM91/YjSb6/qj4y+4aOBs7FpOOYsiTZ2KqM1pZYDAAAAADAMWBmzJLtHD32zJnlf1FVtddzVfWmJG+dWrpDkqf1XF6q6l3zghg4V+M9Ol/MjQEAAAAAhkgYs3xXJLlk6vovkrz5HJ995cz1k/soCPrUNTMmSdY3hTEAAAAAwPAIY5bv789cv3G/rpjpe2eur2ytXdRDTdCbrpkxSbKhMwYAAAAAGCBhzPI9dOb67ef6YFVdn+SDU0uTJA/uoSbozXjPmTHCGAAAAABgeEZHXcAAPWjm+j0HfP49SS6f2e+dhynouGittST3TnLXJGtJ/jrJx6vq00daGAey18wYYQwAAAAAMETCmCVqrd0uyWUzyx854Daz9z/g/Cs6Vv7PJN+f5Etm32itvTfJf0vyiqp697IL42AuWFvrfG9981xP5AMAAAAAODkcU7Zcd0ky3TawkeQTB9zjYzPXdz1URcfH5ZkTxOx4YJLvTfI/Wmuvaa113ccxMB51d8as64wBAAAAAAZIZ8xyXTxz/TdVddBWgZv32fMka0muSvKI1trjq+qgR7zt/wNau2uSSw742H37rmOVjdfMjAEAAAAAmCaMWa7Z4OSW89jjC/vsuWrel+T1Sd6S5N3Z7hT6QpI7Jbl/km9I8o+T3G3qmcuS/GZr7ZFV9Vc91/O9SV7c856DMjq1x8yYTWEMAAAAADA8wpjlunDmev089rh15vp251nLUftgkr9bVW/ueP+TO1/XttZ+LMlPZTuUOeNeSV6R5FsWWCPnobWWydqpuUeSOaYMAAAAABgiM2OWa7YTZnIee1ywz54roao+uEcQM3vvLVX1PdkOZKY9pbX28N6L49DGa/O7Yza2DnoqHwAAAADA6tMZs1w3zVzPdsqci9lOmNk9T7LnJXlCkvtNrT0jyR/2+DNekeQ1B3zmvkle12MNK28yOpWb17fOWjczBgAAAAAYImHMcs0GJ7dvrbWqOki7wEX77HliVdVma+2nk/z01PLjev4Zn8j23Jpz1lr3jJShGq/Nb7pbNzMGAAAAABggx5Qt141JpoOXcZK7HnCPS2euDxQcnABvmrn+8iYNOXY6wxidMQAAAADAAAljlqiqvpDkwzPLlx1wm9n733v+Fa2kj8xcj5Lc6SgKodtkNP9Xi2PKAAAAAIAhEsYs32x48uADPv+gffY76TbmrI2XXgV7Gq/Nb1bacEwZAAAAADBAwpjlu27m+opzfbC1dvckl08tbSR5Tw81rZK7zVxXkk8dRSF06zqmbGPrIOORAAAAAABOBmHM8r1+5vqxB5h5Mjus/pqquqmHmlbJ181c31BVm0dSCZ26jikzMwYAAAAAGCJhzPK9PcmNU9f3SXLlOT77rJnr1/VR0IqZ/QzedCRVsKeuzph1x5QBAAAAAAMkjFmyqjqd5FUzyy/erzumtfaYJI+aWvp8klf3W93x1lp7Rs4Orv7rEZTCPiadx5QJYwAAAACA4RHGHI2rk0wfL/boJC/ourm1dmmSn59ZfnlV3Tjv/qnnaubryvMtuE+ttX/YWnvKAY5nS2vtW3P2Z3Bdkl/rtTh6MV6b/z+tMAYAAAAAGKLRURcwRFV1Y2vtZUleNrX8o621y5K8tKquT5LW2qkkT0zy8iSXTd17fZKfXERtrbULc/ZcljPuM3N999baYzvufXdV3dDx3gOTvDjJB1prr872HJ0/raqbZ2qZ7NTyT7L9OUy7Jcmzq8pE+GOo65iyjS3/cwEAAAAAwyOMOTpXJ7kiyROm1p6d5Ltbax9K8tkk905yx5nnvpDkaVX1mQXVdbckbzzHex+38zXPM3P2cWyz7pfkn+98nW6tfTTJZ7L9b/ziJJcnuXDOcxtJvr2q3nGOdbJk41HHzBidMQAAAADAAAljjkhVnW6tPTXJf0jyD6feWsvZHShnfCrJVVV17aLrOwKnst39c9k+970vybdV1R8tviTO1wVdnTGbwhgAAAAAYHjMjDlCVXVLVX1rkquyPf+ky81JXpHkwVX15mXUtmCvTvKjSf57trtg9rOZ5K1Jvi3JVwpijr+uY8p0xgAAAAAAQ6Qz5hioqtcmeW1r7X5JHpnk0iSTbB/Z9WdJrq2qW85j3/lT1Pd+5oNJDvzcAX/Ge7J9NFlaa2tJHpDtbqB7JvmibP/bb0ry6SR/meSdVXUuoQ3HxHg0/z+hDWEMAAAAADBAwphjpKo+kOQDR13HMlXVVpL37HxxQnR2xmzWkisBAAAAADh6jikDejfpmhmjMwYAAAAAGCBhDNC7rs4YYQwAAAAAMETCGKB3k5EwBgAAAADgDGEM0LvumTHCGAAAAABgeIQxQO/Ga23u+vpWLbkSAAAAAICjJ4wBeueYMgAAAACAXcIYoHddx5QJYwAAAACAIRLGAL2bdIUxZsYAAAAAAAMkjAF6N+44pszMGAAAAABgiIQxQO8ma23u+vrm1pIrAQAAAAA4esIYoHfdM2N0xgAAAAAAwyOMAXrXHcaYGQMAAAAADI8wBuhdVxizebpy+rTuGAAAAABgWIQxQO8mo+5fLRundccAAAAAAMMijAF6N+nojEnMjQEAAAAAhkcYA/RuPGqd761v6owBAAAAAIZFGAP0rmtmTJJsbAljAAAAAIBhEcYAvdvrmDKdMQAAAADA0AhjgN7pjAEAAAAA2CWMAXo3Ge0VxtQSKwEAAAAAOHrCGKB347XW+Z5jygAAAACAoRHGAL3b65iydceUAQAAAAADI4wBemdmDAAAAADALmEM0Lu1Uy1rp+YfVSaMAQAAAACGRhgDLETX3BhhDAAAAAAwNMIYYCEmHUeVrW/WkisBAAAAADhawhhgISajjjBGZwwAAAAAMDDCGGAhxh2dMRubwhgAAAAAYFiEMcBCdIYxOmMAAAAAgIERxgALMV5rc9eFMQAAAADA0AhjgIXo6oxZ36olVwIAAAAAcLSEMcBCXDByTBkAAAAAQCKMARakszNmUxgDAAAAAAyLMAZYiK4wRmcMAAAAADA0whhgIcYdx5StC2MAAAAAgIERxgALMVlrc9c3NmvJlQAAAAAAHC1hDLAQk47OGMeUAQAAAABDI4wBFsLMGAAAAACAbcIYYCG6wpj1TWEMAAAAADAswhhgITrDGJ0xAAAAAMDACGOAhZistbnrjikDAAAAAIZGGAMsRPfMmFpyJQAAAAAAR0sYAyzEZNQVxuiMAQAAAACGRRgDLETnzJhNYQwAAAAAMCzCGGAhujpj1nXGAAAAAAADI4wBFmK81uauO6YMAAAAABgaYQywEF3HlG1s1pIrAQAAAAA4WsIYYCE6wxidMQAAAADAwAhjgIUwMwYAAAAAYJswBliIic4YAAAAAIAkwhhgQbqOKVvfFMYAAAAAAMMijAEWYrzW5q5vbNWSKwEAAAAAOFrCGGAhxmbGAAAAAAAkEcYAC7LXzJgq3TEAAAAAwHAIY4CFmHR0xlQlW6eFMQAAAADAcAhjgIUYd3TGJObGAAAAAADDIowBFmK81jrfW980NwYAAAAAGA5hDLAQXTNjkmR9SxgDAAAAAAyHMAZYiL2PKRPGAAAAAADDIYwBFmI8EsYAAAAAACTCGGBB9jqmTBgDAAAAAAyJMAZYiL3CmFs3hTEAAAAAwHAIY4CFGI9a53sbW7XESgAAAAAAjpYwBliIsWPKAAAAAACSJKOjLmAZWmsXJ3lkkrsk+XSS66rqE0dbFZxso1N7dMY4pgwAAAAAGJCVCmNaaxckuXRq6eNV9Td73H9hkp9I8p1JxlNvnW6t/XqSH6iqjy2kWBi41loma6eyPqcLZt4aAAAAAMBJtWrHlH1fkvfvfP1Zkjt13dhaGyV5Y5JnJ5kkaVNfa0menOQPW2v3XXDNMFiT0fxfMWbGAAAAAABDsmphzLdkN1D59X26Wn4oydfuvJ79y2/t7PGlSV7XWlvru1AgGa/NP6ps3TFlAAAAAMCArEwYs3NE2cOyHaRUktftce8XJ3ludkOXluRtSa5O8sokn8tuQPOgJP94YYXDgI3XujpjhDEAAAAAwHCsTBiT5Cuye9xYklyzx71XJbnDzutK8tKq+vqqemFVfVeSr07yV9kNa75rMSXDsHWFMWbGAAAAAABDskphzH2mXn9mnyPKnrLzvSW5PslLpt+sqg8meVF2g52HtNbu3lOdwI7umTHCGAAAAABgOFYpjDkTllS2A5a5WmujJI/K7nFmv1xVW3NufXWS6fWH9lQnsGPSdUyZmTEAAAAAwICsUhhz0dTrz+9x30OTXJzdrpffnHdTVX0+yQenli4/RG3AHONRm7u+sVVz1wEAAAAATqJVCmOm/6o73uO+r516vZHkHXvc+6mp1190PkUB3cyMAQAAAABYrTDmczvfW5K77XHf3935Xkn+sKpu3ePetanX8/8v/MB56wxjHFMGAAAAAAzIKoUxH516fffW2iWzN7TWbpfksdkOYpLkLfvseaep1zcdrjxgVufMGJ0xAAAAAMCArFIY88c73yvbXSzPmHPPtyW5fXa7XK7p2qy1Nk5yz+wGNzf0UyZwxnita2aMMAYAAAAAGI6VCWOq6sNJ/nTnsiV5SWvt68+831r7qiT/Krvhyl8nefMeW35lkkl2g5sP9FkvkExGXZ0xNXcdAAAAAOAkWpkwZscrsh2eVJKLk1zTWnt3a+26JO9McsnU+6+sqs099vqGqde3JnnPYkqG4eqcGaMzBgAAAAAYkFULY34uyduzG7i0JA9K8pAko6n7bkjyY/vsddXO90ryh1W10W+pQNfMmPVNYQwAAAAAMBwrFcZUVSV5QpK3Zfd4sWT3aLKW5K+SPKmqPtO1T2vtoUkePvXcG/uvFujqjDEzBgAAAAAYktH+txwvOyHL17fWnprt7pb7J7ldkuuzHar8bFV9ep9t/unO9zOBzusWUSsM3XjU5q4LYwAAAACAIVm5MOaMqnpNktec5+PPTvJ9U3t9tpeigNvonBmzWXPXAQAAAABOopUNYw6jqj5/1DXAEExGjikDAAAAAFipmTHAapmYGQMAAAAAIIwBFqf7mDJhDAAAAAAwHIM4pqy19o1Jvi7JXZJ8OskfJXl9Vd16pIXBCdcVxuiMAQAAAACGZKXCmNbapUm+cWrpd6rqY3vc/+VJ/kuSr5zz9g2tte+sqt/uuUxgx3itzV1f36olVwIAAAAAcHRWKoxJ8pwkz995/dkkv9J1Y2vtrknekuRLk5z5i/CZvwC3JPdI8uuttadU1esXUy4M22SkMwYAAAAAYNVmxjwpu8HKf66qv9nj3p9Icred19MhzHQwM0ryqtbal/RdKJBMHFMGAAAAALA6YUxr7Y5JHpDdYKWzm6W1dq8k37Zzb0tyS5KXJnl8kv89yTuzG8rcKcnzFlM1DFvnzJhNYQwAAAAAMByrdEzZV+S2XS1v3+Peb89u0FRJvr2q/uuZN1trr0lybZKH7ez5jCQv7LtgGLpxxzFl6zpjAAAAAIABWZnOmCT3nnp9Q1V9do97n7DzvZL8yXQQkyRVtZ7kRVNL92it3b+fMoEzJmtt7vq6zhgAAAAAYEBWKYy5y873SnJj102ttYuTPDy7x5n9asetv5ft48vOeMhhCwRuq/OYsq2auw4AAAAAcBKtUhhz+6nXN+9x3yOyffzamf9L/m/Pu6mqNpP8xdTS3Q5VHXCW7jBGZwwAAAAAMByrFMZsTr2+3R73PWrq9eeq6k/2uPdzU6/vcF5VAZ0mHTNjNk9XTp/WHQMAAAAADMMqhTFnZsS0JF+2x32P2fleSd6+z57Toc7WedYFdOjqjEmSjdO6YwAAAACAYVilMOYDU6+/pLX2gNkbWmtfmuSK7M6LefM+e9556vXnOu8CzstkjzBmfVMYAwAAAAAMwyqFMe9Kcjq7QcsPzLnnOdn+N52ZF/Omrs1aa3dIcunUfh/up0zgjPGodb63seWYMgAAAABgGFYmjKmqTyf5b9kOWlqS72mt/XBr7ZLW2h1ba89J8oLshisfqKp37bHl10ztlSTvX1DpMFh7HlO2pTMGAAAAABiGlQljdly9872yHaK8OMnHk3wqycuTjHbWK8m/2WevJ0y9/kxVCWOgZ44pAwAAAABYsTCmqt6U5N9nN3BJdrtbptfekeTn99nuqp37K8m1vRcLZDLSGQMAAAAAsFJhTJJU1fcn+VdJNrN7xNgZLckbkzyxqra69mitPSHJvaae/60FlAqDt9cxZevCGAAAAABgIEZHXcD5qKoXtdZ+JsmTktw/ye2SXJ/kjVX1389hi29K8idT17/ef5XAeG02L921sVmd7wEAAAAAnCQrGcYkSVVdn+RnzvPZ5/RcDjCHzhgAAAAAgBU8pgxYHZM9whgzYwAAAACAoRDGAAtz6lTL6NT8o8qEMQAAAADAUAhjgIXqOqpMGAMAAAAADMXKzoyZ1Vr78iT/W5LLktwpyR2SfD7Jp5N8KMk7quoDR1chDNN4reULG2evr28KYwAAAACAYVjpMKa1drckz07yXUm+9BzuvyHJzyX5f6rq4wsuD0gyGc3vjFnfqiVXAgAAAABwNFb2mLLW2ncneX+S/zvJ3ZK0qa9iXShGAAAgAElEQVTb3Dr1dY8kP5zkfa2171petTBcnceU6YwBAAAAAAZiJcOY1trPJfmZJBdlO2Spna+/vSVnBzNn7mlJLk7ys621/3cpBcOAmRkDAAAAAAzdyh1T1lp7aZJn7VyeCVdako8muSbJnyS5McnN2Q5r7pLkq5JcmeTLshvatCTPaq39VVW9aFn1w9B0HVMmjAEAAAAAhmKlwpjW2lckeUFuG6j8jyTPS/LGqtpzCEVr7XFJfjzJQ7Ib5LygtfYrVfXuhRUOA9bVGWNmDAAAAAAwFKt2TNm/TLI2df1LSb6mqn53vyAmSarqd5N8TZJfzO7xZmtJXrKAWoEkk7XZMU7b1s2MAQAAAAAGYmXCmNbahUm+KbtdMW+rqu+oqq2D7FNVp6vqmUnemt0jzh6/sz/QMzNjAAAAAIChW5kwJsnXJbkw2+FJkjz/kPu9YOr1hTv7Az0TxgAAAAAAQ7dKYcyXTb3+ZFX9f4fZrKrekeQTHfsDPRmPumbGCGMAAAAAgGFYpTDmrjvfK8lHetpzep9LetoTmDLp6ozZ3HfMEwAAAADAibBKYcwtU69v39Oet5t6fWtPewJTJqM2d90xZQAAAADAUKxSGHPmSLGW5PLW2qECmZ3n7z1nf6BHXTNj1jeFMQAAAADAMKxSGPOene+V5MIkTz/kfk/PbTtj3n3I/YA5usIYnTEAAAAAwFCsTBhTVX+S5Pqdy5bk6tbaPc9nr9bapUmuznawkyTXV9WfHr5KYFZnZ4wwBgAAAAAYiJUJY3a8KttBTCW5S5K3tdYedpANWmtfneStO8+f2etVvVYJ/K3JmpkxAAAAAMCwrVoY82NJPrnzupJcluQdrbVfbK1d2VobzXuotTbaef9VSf4gyeXZ7Yr5xM6+wAJMRl3HlNXcdQAAAACAk2ZueHFcVdVNrbWnJfmtJBdkO1BZS/KMna+N1tp7k9yY5OYkFyW5c5IHJpnsbHOmG6YluSXJ06rq5mX+O2BIzIwBAAAAAIZupcKYJKmqt7TWnprkPya5Y3Y7XFq2A5eHTK2dWf/bx7MbxHwmyTOq6q0LLxoGrCuMuXVTGAMAAAAADMOqHVOWJKmqN2Q7dHnDztKZwOVM2HKb23PbwCZJfiPJ/1JVv7nIOoG9jikTxgAAAAAAw7BynTFnVNVHk/yD1tr9kzwryaOTPDS7x5FNW0/yx0nekuSVVfX+pRUKAzdea3PXhTEAAAAAwFCsbBhzRlW9L8kLkqS1NklytyR3SnJxkpuSfDrJDVW1cWRFwoBNumbGbM42sQEAAAAAnEwrH8ZMq6r1JB/e+QKOgbFjygAAAACAgVvJmTHA6hh3dMasC2MAAAAAgIEQxgAL1XVM2fqmMAYAAAAAGAZhDLBQXZ0xjikDAAAAAIbiWMyMaa39whGXUFX1rCOuAU6k8Vqbu76xVUuuBAAAAADgaByLMCbJdyQ5qr/Mtp2fLYyBBZiMzIwBAAAAAIbNMWXAQnXNjNnYOp0q3TEAAAAAwMl3XDpjku0OFeCEGXd0xlQlW6cro45jzAAAAAAATorjEsbc+6gLABZj3NEZk2wfVTba430AAAAAgJPgWIQxVfWho64BWIzxHp0vG5uVTJZYDAAAAADAEfB/SQcWqmtmTLLdGQMAAAAAcNIJY4CFmnTMjEmSDWEMAAAAADAAwhhgofaaGSOMAQAAAACGQBgDLNReYcz6pjAGAAAAADj5hDHAQpkZAwAAAAAM3eioC2BXa+2+SR6R5J5JJkk+neS9Sd5eVbccZW3L0lq7c5KvTXLfJBcluTnJ/0xybVV96ihr4/yMR63zvY2tWmIlAAAAAABHQxhzDLTWnpzkRUm+puOWm1prr0rykqq6cUk1tSQPzHY49Igkj0zykCTjqdt+saq+o6ef91VJfiTJEzK/Y2urtfaGJC+qqj/t42eyHGbGAAAAAABDJ4w5Qq21C5K8Msm373PrxUmek+TprbWrqur3F1jTM3fqeXiSL17Uz5n5mf8kyU9k7/8e15I8Mck3t9aeW1X/bhm1cXijUy2tJTWnCWbDzBgAAAAAYADMjDkirbVTSX41ZwcxW0n+Msl1ST47894lSX6rtfZ3Fljak5I8JssLYp6b5KdydhBzQ5I/2vk+bZTkp1trP7CE8uhBa62zO8bMGAAAAABgCIQxR+d52Q4+pv1sksuq6j5V9dVJviTJU5J8eOqe2yd5dWttKWHJjJv73Ky1dkWSH59ZfnOSh1XVParq4VV1jyT/a5K3zNz3k621R/RZD4sz6QpjdMYAAAAAAAMgjDkCO0Pqf2hm+YVV9eyquv7MQlWdrqpfS3JFkg9O3XvPJM9dcJkfT/Ib2Z5l801J7pzto8T69K+zffzYGb+R5Bur6l3TN1XVHyZ5XJI3TC2Pdp5nBYzX2tz1ja05Z5cBAAAAAJwwwpij8fwkd5i6/v0kV3fdXFUfS/KdM8s/uBPq9O1Hst2dc/eqemJVvbSqfqeq/rrPH9Jae3y2Q6YzPpXkWVW1Pu/+nfV/tHPfGV/fWvuGPutiMbqOKdtwTBkAAAAAMADCmCXbmRXzzJnlf1E1b7z5rqp6U5K3Ti3dIcnTei4vVfWuqvpI3/vOMRsu/fuq+uReD1TVJ5K8Yp99OIbMjAEAAAAAhkwYs3xXJLlk6vovsj0n5Vy8cub6yX0UtGyttQuSfOPM8i+c4+Oz9z2+tTY5fFUs0gUjnTEAAAAAwHAJY5bv789cv3G/rpjpe2eur2ytXdRDTct2ZZLpuv+8qj50Lg9W1QeTvH9q6Q5JHt1bZSxE5zFlm8IYAAAAAODkE8Ys30Nnrt9+rg9W1fVJPji1NEny4B5qWrbz/gx2XLvPfhwz41Gbu+6YMgAAAABgCIQxy/egmev3HPD52ftn91sFPoOB6eyM2TrXpjAAAAAAgNUljFmi1trtklw2s/yRA24ze/8Dzr+iIzNb8xA/g0HpCmPWHVMGAAAAAAzA6KgLGJi7JJk+r2kjyScOuMfHZq7veqiKjsZszR894PML+wxaa3dNcskBH7tvXz//pJp0dsYIYwAAAACAk08Ys1wXz1z/TVUd9Jymm/fZcxXM1jz7b9rPIj+D703y4h73I8lkJIwBAAAAAIbLMWXLNRsa3HIee3xhnz1XwWE/h5PwGQzKeK3NXTczBgAAAAAYAmHMcl04c71+HnvcOnN9u/Os5Sgd9nM4CZ/BoHTNjLnVzBgAAAAAYAAcU7Zcsx0gk/PY44J99lwFtyS5/dT1QT+HRX4Gr0jymgM+c98kr+uxhhPHzBgAAAAAYMiEMct108z1bIfIuZjtApndcxXclNuGMQf9HBb2GVTVJ5J84iDPtDb/CC52dXXGCGMAAAAAgCFwTNlyzYYGt28H/0v+RfvsuQpma579N+3nJHwGgzIZCWMAAAAAgOESxizXjUmmJ5aPk9z1gHtcOnN9oC6OY2K25nse8PmT8BkMSldnzPpmzV0HAAAAADhJhDFLVFVfSPLhmeXLDrjN7P3vPf+Kjsyfz1wP8TMYlPFofgOYzhgAAAAAYAiEMcs3Gxw8+IDPP2if/VaBz2BgJp2dMcIYAAAAAODkE8Ys33Uz11ec64OttbsnuXxqaSPJe3qoadnO+zPY8bX77Mcx03VMmc4YAAAAAGAIhDHL9/qZ68e21uaf4XS2x81cX1NVqzi8/s1Jbp66vn9r7V7n8mBr7fIkXz619Pmd/TjGhDEAAAAAwJAJY5bv7UlunLq+T5Irz/HZZ81cv66Pgpatqm5J8rszy//oHB+fve+3q2r98FWxSJNRxzFlW7XkSgAAAAAAlk8Ys2RVdTrJq2aWX7xfd0xr7TFJHjW19Pkkr+63uqV65cz197XWLtnrgdbaXZN87z77cAxN1ub/560zBgAAAAAYAmHM0bg6yfTxYo9O8oKum1trlyb5+Znll1fVjfPun3quZr6uPN+C+1ZVb0jyjqmlOyd5ZWttPO/+1tok28HLnaeW31pVv7O4KumLY8oAAAAAgCEbHXUBQ1RVN7bWXpbkZVPLP9pauyzJS6vq+iRprZ1K8sQkL09y2dS91yf5yUXU1lq7MMnXdbx9n5nru7fWHttx77ur6oZ9ftzzkrwlu6HgP0jyu621/6uq3jVV08Oy/e999NSzW0mev8/+HBNdYcz6pjAGAAAAADj5hDFH5+okVyR5wtTas5N8d2vtQ0k+m+TeSe4489wXkjytqj6zoLruluSN53jv43a+5nlmzj6O7Taq6m2ttRdm+7M448okf9Rauz7JDUnukeTucx5/flW9Y846x9C4Y2aMzhgAAAAAYAgcU3ZEdmbHPDXJr8y8tZbtDpSvztlBzKeSfHNVXbv4Cpejqn48yT/NdqfLtHskeVjODmK2kvxgVf2bJZRHT7pmxuiMAQAAAACGQBhzhKrqlqr61iRXJbluj1tvTvKKJA+uqjcvo7ZlqqqfTPLwJG9I0vXX+dNJXp/kYVX1U8uqjX5MOjtjasmVAAAAAAAsn2PKjoGqem2S17bW7pfkkUkuTTJJ8pkkf5bk2qq65Tz2nd+OsPczH0xy4OcOq6quS/KE1tpdsj2z5j5JLsp2EPU/s/0Z3LjsuuhH18wYx5QBAAAA8P+zd+dRlt7lfeC/7621a+u9WvsuISSxSmLfxOIggnfjGNvj4CGxh0nG9ozHZA/OSWJsx1s4Z7wEGzPBsRNjYjOOkY0DCLBACIERSEgISa29u6urt9r3d/64Vd3VpXt7rbpV99bnc06de9/f712eFpwWp748zwubgTBmAynL8pEkj6x3HetpMXD5s/Wug9VVL4yZWyizsFCmUml4/gcAAAAA0DDGlAFrrl4YkyQzumMAAAAAgBYnjAHWXOcpwhijygAAAACAVieMAdZcR3v9MWSz82UDKwEAAAAAaDxhDLDmdMYAAAAAAJuZMAZYc6d8Z8ycMAYAAAAAaG3CGGDNdbafIozRGQMAAAAAtDhhDLDmTtUZY0wZAAAAANDqhDHAmutoK+ruzc6VDawEAAAAAKDxhDHAmjvlO2N0xgAAAAAALU4YA6y5TmPKAAAAAIBNTBgDrLlKpUh7pfaoMmEMAAAAANDqhDFAQ9QbVTYzJ4wBAAAAAFqbMAZoiI42nTEAAAAAwOYkjAEaorO9TmfMfNngSgAAAAAAGksYAzREZ50xZbPGlAEAAAAALU4YAzRER53OGGPKAAAAAIBWJ4wBGqKjXmeMMAYAAAAAaHHCGKAh6oUx08aUAQAAAAAtThgDNERnW1FzfXa+bHAlAAAAAACNJYwBGsKYMgAAAABgsxLGAA3R2S6MAQAAAAA2J2EM0BD1OmNmhDEAAAAAQIsTxgANUXdM2Zx3xgAAAAAArU0YAzREZ3tRc31mfr7BlQAAAAAANJYwBmgInTEAAAAAwGYljAEaom4Y450xAAAAAECLE8YADdHZXvuvmxlhDAAAAADQ4oQxQEN06owBAAAAADYpYQzQEB1tRc312XnvjAEAAAAAWpswBmiIeu+MmZnTGQMAAAAAtDZhDNAQdcMYY8oAAAAAgBYnjAEaorPdO2MAAAAAgM1JGAM0RGedzhhhDAAAAADQ6oQxQEN0tBU112fnygZXAgAAAADQWMIYoCE6jCkDAAAAADYpYQzQEB11xpRNzwljAAAAAIDWJowBGsI7YwAAAACAzUoYAzREvc6Yydn5BlcCAAAAANBYwhigIbb3dNRcH52ay5RABgAAAABoYcIYoCEGB7rr7h0cnW5gJQAAAAAAjSWMARpicKCr7t6QMAYAAAAAaGHCGKAh+rva091R+6+cg6NTDa4GAAAAAKBxhDFAQxRFkcH+2qPKDozojAEAAAAAWpcwBmiYPXVGlQ3pjAEAAAAAWpgwBmiYep0xQzpjAAAAAIAWJowBGmZ3f73OGGEMAAAAANC6hDFAwwzWGVN2YMSYMgAAAACgdQljgIapN6bsoM4YAAAAAKCFCWOAhtlTpzPm0PhMZucXGlwNAAAAAEBjCGOAhqnXGZMkw2O6YwAAAACA1iSMARpmsL92Z0ySHBgRxgAAAAAArUkYAzTMtp6OdLbV/mtnaGSqwdUAAAAAADSGMAZomKIosrtOd8zQqM4YAAAAAKA1CWOAhhocEMYAAAAAAJuLMAZoqHrvjTk4akwZAAAAANCahDFAQw32d9dcPzCiMwYAAAAAaE3CGKCh9tQdU6YzBgAAAABoTcIYoKHqdcYM6YwBAAAAAFqUMAZoqN11OmOGx6Yzv1A2uBoAAAAAgLUnjAEaarC/dhizUCaHxnTHAAAAAACtRxgDNFS9MWVJMjQqjAEAAAAAWo8wBmionb2daasUNfeGRqcaXA0AAAAAwNoTxgANVakU2d1Xe1TZ0IjOGAAAAACg9QhjgIYbHKgTxhhTBgAAAAC0IGEM0HCD/bXDmAMjxpQBAAAAAK1HGAM03OBAd811nTEAAAAAQCsSxgANV68zRhgDAAAAALQiYQzQcIP9tTtjDhpTBgAAAAC0IGEM0HCn6oxZWCgbXA0AAAAAwNoSxgANt6fOO2PmFsocmZhpcDUAAAAAAGtLGAM03OBA7c6YxHtjAAAAAIDWI4wBGm5nb2eKovaeMAYAAAAAaDXCGKDh2tsq2dlbuzvmwMhUg6sBAAAAAFhbwhhgXeypM6rsoM4YAAAAAKDFCGOAdTHYXzuMGdIZAwAAAAC0GGEMsC4G+7trrntnDAAAAADQaoQxwLoYrDOmTBgDAAAAALQaYQywLuqNKTtgTBkAAAAA0GKEMcC6GByoP6asLMsGVwMAAAAAsHaEMcC6qNcZMzO3kJHJuQZXAwAAAACwdoQxwLqo1xmTJEOjRpUBAAAAAK1DGAOsi919tTtjkuTAyHQDKwEAAAAAWFvCGGBddLZXsqO3s+aezhgAAAAAoJUIY4B1U++9MUOjOmMAAAAAgNYhjAHWze56YYwxZQAAAABACxHGAOtmsL+75voBY8oAAAAAgBYijAHWzZ6B2p0xB3XGAAAAAAAtRBgDrJv674zRGQMAAAAAtA5hDLBuBgdqjykbGtUZAwAAAAC0DmEMsG7qdcZMzMxnbHquwdUAAAAAAKwNYQywbgb7a3fGJMmBEaPKAAAAAIDWIIwB1s3gQO3OmCQZGjGqDAAAAABoDcIYYN10d7RloLu95t7QqM4YAAAAAKA1CGOAdTU4UHtU2cFRnTEAAAAAQGsQxgDrarC/9qgy74wBAAAAAFqFMAZYV3vqdMYM6YwBAAAAAFqEMAZYV/U6Y4ZGhDEAAAAAQGsQxgDrane9MGbUmDIAAAAAoDUIY4B1NWhMGQAAAADQ4oQxwLraU6czZnRqLpMz8w2uBgAAAABg9QljgHVVrzMmMaoMAAAAAGgNwhhgXQ3W6YxJjCoDAAAAAFqDMAZYV71d7entbKu5NzQijAEAAAAAmp8wBlh39UaVHRgxpgwAAAAAaH7CGGDd1RtVZkwZAAAAANAKhDHAuqvXGTM0qjMGAAAAAGh+whhg3dXrjDmoMwYAAAAAaAHCGGDd1QtjvDMGAAAAAGgFwhhg3e2pO6ZMZwwAAAAA0PyEMcC6q9cZc3RiNtNz8w2uBgAAAABgdQljgHU3OFA7jEm8NwYAAAAAaH7CGGDd7e6vPaYsMaoMAAAAAGh+whhg3Q10t6e7o/ZfR0MjUw2uBgAAAABgdQljgHVXFEUG63TH6IwBAAAAAJqdMAbYEAb7a783ZmhEGAMAAAAANDdhDLAhDA7UCWNGjSkDAAAAAJqbMAbYEOqNKTugMwYAAAAAaHLCGGBDqN8ZI4wBAAAAAJqbMAbYEOp1xhw0pgwAAAAAaHLCGGBDGOyv3RlzaHwmc/MLDa4GAAAAAGD1CGOADaHemLKyTIbHZhpcDQAAAADA6hHGABvCnjpjypLkwIhRZQAAAABA8xLGABvCtp6OdLbV/itpaHS6wdUAAAAAAKweYQywIRRFkd113hszNKozBgAAAABoXsIYYMOoG8aM6IwBAAAAAJqXMAbYMPYM6IwBAAAAAFqPMAbYMAb7u2uu64wBAAAAAJqZMAbYMAbrvjNGGAMAAAAANC9hDLBhDBpTBgAAAAC0IGEMsGEMDtQeU3ZwdDrzC2WDqwEAAAAAWB3CGGDDqDembKFMDo0bVQYAAAAANCdhDLBhDPbX7oxJkqERYQwAAAAA0JyEMcCGsbO3M22VoubewVFhDAAAAADQnIQxwIZRqRTZ1ddZc29odKrB1QAAAAAArA5hDLCh7BmoParsgDFlAAAAAECTEsYAG8pgf1fNdZ0xAAAAAECzEsYAG8ru/tqdMUM6YwAAAACAJiWMATaU+p0xwhgAAAAAoDkJY4ANpd47Y4ZGjCkDAAAAAJqTMAbYUOp1xhwcm05Zlg2uBgAAAADg/AljgA1lcKB2GDM7X+bIxGyDqwEAAAAAOH/CGGBDGeyvPaYsSYZGjSoDAAAAAJqPMAbYUHb1daYoau8NjUw3thgAAAAAgFUgjAE2lPa2Snb21h5VdmBEZwwAAAAA0Hza17sATiiK4uokL0tySZLOJEeSPJTkC2VZrttvoYuiKJK8NMmLkwwuLh9Icl+Sr5beqs4qG+zvyvDYc7tghkZ1xgAAAAAAzUcYswEURfE9Sf5VqoFHLWNFUXw4yb8py3K4gXV1JPnpJD+T5OI6pz1dFMVvJPlAWZZn/Hb1oijekOQz51HeE2VZXnEe17OBDQ505Zv7nrt+UBgDAAAAADQhY8rWUVEUXUVR/EGSP039ICZJ+pL84yTfLIridQ2q7dIkX0ryH1I/iEmqXTy/kuSLRVGc6jw4Y4P9tceUDY0aUwYAAAAANB9hzDopiqKS5L8l+ZEVW/NJ9ib5WpJjK/Z2J7mjKIpXrnFtg6l2rbxkxdZkkgeSPJhk5W/Fb07ymaIodq1lbWwOewa6a64fGNEZAwAAAAA0H2PK1s/PJfnuFWu/neTflmX5bHI8sPnuJL+R5LLFc3qS/HFRFDeVZbkyrFktH05y9bLjqST/NMkHy7KcWKytN8lPJPmFJEu/Ob82yYeSfNc5PPMjSf7zWZw/eQ7PoEnojAEAAAAAWokwZh0URbEzyb9YsfzPyrL8xeULZVkuJPnToijuSfI3Sa5Y3Lokyf+V5H1rUNt3JLl92dJskr9TluXnVtQ2nuTXi6L4apK/TtKxuPWdRVHcVpbl2b4P5rGyLP/nudZNa9ndX7szZmhkOmVZpiiKBlcEAAAAAHDujClbH+9N0r/s+HNJfqneyWVZPpPkH6xY/j8XQ53V9m9XHP/iyiBmubIsP5vn1v7vVr0qNpXBgdqdMdNzCxmZmmtwNQAAAAAA50cY02CLo8d+fMXyz5dlWZ7qurIsP5Xk88uW+pP84CrX9oIkL1u2NJ7kP5zBpb+8eO6SVxVF8fzVrI3Npd47Y5JkaMSoMgAAAACguQhjGu9VSXYvO34syZ1neO3vrTj+ntUoaJmV77D547IsR0930eI5H12xvNq1sYns7qvdGZMkQ6PTDawEAAAAAOD8CWMa7++uOP7r03XFLD93xfEbiqLoXYWalqys7ZNnce3K2t5+nrWwiXW2V7K9p6Pm3tCozhgAAAAAoLkIYxrvxSuOv3CmF5Zl+WySx5ctdSa5YRVqSlF9I/oLVyyfcW1J7lpx/KLCW9Y5D4P9tUeVDY3ojAEAAAAAmoswpvFWvkvlm2d5/crzV+vdLJcn6Vl2PF6W5ZNnenFZlk8kmVi21Jvk0rMpoKi6qiiKVxRF8eqiKJ5fFMX2s7kHrWNwoPaoMmPKAAAAAIBmI4xpoKIotiS5bMXyU2d5m5XnP+/cKzrlfc62rlrXnE1tfz/JcJJHk3wxyd+kGjwdLoriwaIo/p+iKG48h5poUvU6Yw6MGFMGAAAAADSX9vUuYJPZlWT56K7ZJENneY9nVhwPnldF9e/z9Dnc45mcHMCcTW1XnGLv+sWf9xRF8bEkP1mW5eGzL+/0iqIYTLL7LC+7ei1q2ex0xgAAAAAArUIY01h9K44nyrIsz/Ie46e557laeZ+VzzkTa1XbkiLJDyR5WVEUt5dlebYj3s7E/57kfWtwX87SYH/tMOagMAYAAAAAaDLGlDXWynDiXOYtTZ7mnudqvWp7OMmvJfnuJNckGUjSkWpXzWuS/Jsk+1dcc1mSTxRFseccaqRJ7BmoPaZsyJgyAAAAAKDJ6IxprJW/XZ45h3usbAvYco61rNTo2h5PcltZlnfW2T+4+HNXURS/mOQ3kvzksv3Lk/xmku8/hzppAvU6Y8Zn5jM2PZe+Ln99AQAAAADNwW8zG2vl/6W/8xzusfI31KvVJtDQ2sqyfDzVQOa0yrKcSvK/FUUxmeRnlm19X1EUt5Rlee9Z1nkqv5nko2d5zdVJPr6KNZBksL92Z0xS7Y7p273aU/AAAAAAANaGMKaxxlYc1/9tc30ru01W3vNcbeTalvxckrenOs5syY8mWbUwpizLoSRDZ3NNURSr9XiWGRyo3RmTJEOj07lKGAMAAAAANAnvjGmsleFET3H2v8nvPc09z9XK+6x8zplYq9qSJGVZziX5wIrl71jNZ7BxdHe0pb+7dl48NLpyIh4AAAAAwMYljGms4STlsuOlF9WfjYtXHJ9VF8cprLzPJedwj7WqbblPrTi+9hwCLZrEnoHaDVpDI6s1nQ8AAAAAYO0JYxqoLMvJJE+uWL7sLG+z8vyHzr2ik3xrxfGl53CPldesVm3LPbXiuD3J9jV4DhvAYH/tUWU6YwAAAACAZiKMabyVAcUNZ3n9809zv3P1RJLJZce9RVFcfqYXL57bs2xpPM8NTlbDbI21jjV4DhtA3TBGZwwAAAAA0ESEMY33tRXHrzrTC4uiuDDJFcuWZpN8cxVqSlmWZZKvr1g+49qSvHrF8dcX77naLlhxXCY5tAbPYQOoN6bsb586mrX5rxcAAAAAwOoTxjTe/1hx/OazeOfJypfVf6Ysy7FVqMW17FIAACAASURBVGnJytrechbXrjz3z8+zlnpes+J4X1mWc2v0LNbZtXv6a64/cWgiD+4bbXA1AAAAAADnRhjTeF9IMrzs+KokbzjDa9+94vjjq1HQMv/fiuN3FEXRd7qLiqLoT/KOFcurXduSlf8MPrVGz2EDuO15u1OpE1Xecf++xhYDAAAAAHCOhDENVpblQpIPr1h+3+m6Y4qieFOS1y5bGk3yx6tc29eTfHnZUl+S957Bpe9N0rvs+O6yLFdlfNpyRVH8aJ4bXP3Zaj+HjWNnX1decdXOmnt33L+/wdUAAAAAAJwbYcz6+KUky8eLvT7JP6l3clEUFyf53RXL/7Esy+Fa5y+7rlzx84YzqO1frzj+p0VRvO4Uz6hV+788TV0/VBTF953FeLYURfHOPPefwdeS/OmZ3oPmdPtNK18TVPXI0Fi+fcCoMgAAAABg4xPGrIPFEOUXViy/vyiK3yyK4qKlhaIoKkVRfE+qo82uWHbus0l+dY1q+8skn1y21JHkr4qi+OmiKHqW1dZbFMXPJPnLxXOWfKIsy9ONDrs+yceSPFwUxb8viuKVRVH0rjypKIrOoijeWBTFx5P8YZKuZdtTSd5Teot7y/s7N16QerGd7hgAAAAAoBkIY9bPLyX5HyvW3pPkyaIoHi2K4qtJDqXa+XHZsnMmk/xgWZZH17C2H0uyd9lxd5LfSDJcFMX9RVE8kOp7b359cW/Jo0nedRbPuSbJP081bBopiuKJoijuK4ri7qIoHkxyLNV3wnzXiutmk/xIWZZ3n8WzaFKDA9255fLtNfeEMQAAAABAMxDGrJPFd8e8I8l/XbHVluSqJC9Jsm3F3qEkbyvL8q41ru1AktuS3Ldia0uSG5PckJNDmKQ6Muy2siwPnuNjK6mGTi9M8vJUu2dWPiNJHk7yyrIs//s5Pocm9NabLqy5/uC+kTw+PN7gagAAAAAAzo4wZh2VZTlVluU7k/xAqmFGPeNJfjPJDWVZ3tmg2p5I8rJU3wfz7ClOfTbJe5O8vCzLp87w9n+c5P1Jvphqp8/pzCX5fJIfTnJTWZZfOcPn0CLeWue9MYnuGAAAAABg42tf7wJIyrL8WJKPFUVxTapdIRcn6UxyNMmDSe4qy3LqHO5b500bZ3z9TJJfLoriV5LcnORFSQYXt4dSDZC+utjlczb3/Waq48lSFEVbkuel2g10SZKBVP/sY0mOpDou7ctlWZ5JaEOLunjblrzo0m2576nnTue74/59ec8brl6HqgAAAAAAzowwZgMpy/KRJI+sdx0rLYYtX178We17zyf55uIP1PW2my6oGcZ8/eljefrIRC7Z3rMOVQEAAAAAnJ4xZUBTuL3Oe2OS5C+NKgMAAAAANjBhDNAULtvZkxsvGqi5570xAAAAAMBGJowBmsbtN11Qc/0rTxzJ/mNn/VolAAAAAICGEMYATeOtpxhV9lcP6I4BAAAAADYmYQzQNK4Z7Mt1e/pq7t1x/74GVwMAAAAAcGaEMUBTqdcdc8/ewxkem25wNQAAAAAApyeMAZrK215Q+70xC2XyyQcONLgaAAAAAIDTE8YATeV5e/pz5a7emntGlQEAAAAAG5EwBmgqRVHkrTfV7o754qOHcnRipsEVAQAAAACcmjAGaDpvq/PemLmFMn/9TaPKAAAAAICNRRgDNJ2bLh7IJdu31Ny74/79Da4GAAAAAODUhDFA0ymKIrfXGVX2N98ezsjUbIMrAgAAAACoTxgDNKW31hlVNjO/kE8/ONTgagAAAAAA6hPGAE3pJZduy56Brpp7d9y/r8HVAAAAAADUJ4wBmlKlUuT2Ot0xd37rYMan5xpcEQAAAABAbcIYoGm9tc57Y6bnFnLntw42uBoAAAAAgNqEMUDTuvWKHdnV11lzz6gyAAAAAGCjEMYATautUuQ7bqzdHfPph4YyNTvf4IoAAAAAAJ5LGAM0tdvrjCqbmJnP5x42qgwAAAAAWH/CGKCpveKqndnW01Fz74779ze4GgAAAACA5xLGAE2to62Stzx/T829//nggUzPGVUGAAAAAKwvYQzQ9N72ggtrro9OzeULjxxqcDUAAAAAACcTxgBN71XX7Ex/V3vNvTvu39fgagAAAAAATiaMAZpeV3tb3nxD7VFln/zmgczOLzS4IgAAAACAE4QxQEt4600X1Fw/OjGbLz12uMHVAAAAAACcIIwBWsLrr9udns62mntGlQEAAAAA60kYA7SE7o623Hb9YM29v3pgf+YXygZXBAAAAABQJYwBWsbtdUaVDY/N5G8eGW5wNQAAAAAAVcIYoGXc9rzBdLXX/mvtfR+/P5Mz8w2uCAAAAABAGAO0kN6u9rz+ut019x4/NJFf+eS3GlwRAAAAAIAwBmgx73r1FXX3PnTX3nz58cONKwYAAAAAIMIYoMW86upd+f6XXlJzryyT9/7J140rAwAAAAAaShgDtJx//fYbsmegq+be3uFx48oAAAAAgIYSxgAtZ2tPR97/fS+ou/+hu/bmXuPKAAAAAIAGEcYALemN1+/J97304pp7ZZn83J98PVOzxpUBAAAAAGtPGAO0rPe9/cYM9p9iXNlfGVcGAAAAAKw9YQzQsk43ruz37tqbrzxhXBkAAAAAsLaEMUBLe9PzTzOu7KPGlQEAAAAAa0sYA7S8U40re2x4PL/6SePKAAAAAIC1I4wBWt7Wno78wvfWH1f2u39jXBkAAAAAsHaEMcCm8OYb9uT7XmJcGQAAAADQeMIYYNN433eeelzZr/31ww2uCAAAAADYDIQxwKZxunFlH/z8Y/nKE0caWBEAAAAAsBkIY4BN5bTjyv7kPuPKAAAAAIBVJYwBNp1//Z03ZHe9cWUHjSsDAAAAAFaXMAbYdLb1dOb9pxhX9rvGlQEAAAAAq0gYA2xKb75hT763zriyhTL5qT/62zx1eKLBVQEAAAAArUgYA2xa7zvFuLJnjk7mh/7T3XnykEAGAAAAADg/whhg09rW05lfOMW4smog88U8cWi8gVUBAAAAAK1GGANsam85xbiyJHn22FT+3u/cnb3DAhkAAAAA4NwIY4BN799+z0154SVb6+7vH5nK3/udL+bRg2MNrAoAAAAAaBXCGGDT6+tqz0fe/fK8+NJtdc8ZGp3O3/udu/PtA6MNrAwAAAAAaAXCGIAkW7d05CPvflleeln9QGZ4bDrv/ODd+dZ+gQwAAAAAcOaEMQCL+rs78p/f/fLcesX2uucMj83knR+8Ow/uG2lgZQAAAABAMxPGACzT19WeD//4y/KyK3fUPefweDWQeeDZYw2sDAAAAABoVsIYgBV6u9rz4R+/Na+8amfdc45OzOaHP/il3P+MQAYAAAAAODVhDEANPZ3t+dC7bs1rrtlV95xjk7P54Q/enfueOtrAygAAAACAZiOMAahjS2dbfvfv35LXXls/kBmZmsuP/u6X8tUnjzSwMgAAAACgmQhjAE6hu6MtH/yxW/KG5+2ue87o9Fx+7PfuyZ3fGmpgZQAAAABAsxDGAJxGd0dbfud/uTlvun6w7jlj03N51+9/Ob/0lw9lbn6hgdUBAAAAABudMAbgDHS1t+W3fvTmvOWGPac877fufDQ/9J/uzrNHJxtUGQAAAACw0QljAM5QZ3slv/kjL83tN11wyvPufeJI3vaBz+dTDx5oUGUAAAAAwEYmjAE4Cx1tlXzgnS/J333Bhac87+jEbN79/96bf/8X38yssWUAAAAAsKkJYwDOUkdbJf/xh16cn3jdVac994Of35t3/PYX8/SRiQZUBgAAAABsRMIYgHPQ3lbJP3/b8/Ohd92SbT0dpzz3a08dzdv+4+fzyQf2N6g6AAAAAGAjEcYAnIc3Xr8nn/ip1+aWy7ef8ryRqbn8xEe+kn/z5w9kZs7YMgAAAADYTIQxAOfpom1b8kc/8Yq85w1Xn/bc37/r8bzjt7+Qpw4bWwYAAAAAm4UwBmAVdLRV8k/een0+/OO3Zkdv5ynPve/pY3nbBz6fv/j6vgZVBwAAAACsJ2EMwCp6w/MG84mfem1eduWOU543OjWXf/SHX827P/zlPHlIlwwAAAAAtDJhDMAqu2Brd/7wH7w8/8cbr0lRnPrcTz00lLf8+mfzgU99O1Oz840pEAAAAABoKGEMwBpob6vkZ7/jefnI//ry7Oo79diy6bmF/NpfP5y3/sbn8tmHDzaoQgAAAACgUYQxAGvoNdfuyid+6rV55VU7T3vu44cm8vc/dE/e8wdfybNHJxtQHQAAAADQCMIYgDU2ONCdP/gHL8/PvPnaVE4ztixJ7rh/f978a5/Nb3/20czMLax9gQAAAADAmhLGADRAW6XIz7z5unz8H70mL7p022nPn5iZzy/e8VDe9oHP54uPHmpAhQAAAADAWhHGADTQCy7Zmj99z6vyC9/7gmzd0nHa8x8ZGss7P3h3fvq//m2GRqYaUCEAAAAAsNqEMQANVqkU+eGXX5ZP/+zr84O3XHJG13z8a8/mTb/62fzWnY9mYmZujSsEAAAAAFaTMAZgnezs68ov/8CL8rH3vDLPv3DgtOePTs/ll/7yobzulz+T3/38Y5manW9AlQAAAADA+RLGAKyzmy/fkT//x6/O+77zhvR3tZ/2/OGxmfy7v3gwr/3lz+TDd+0VygAAAADABieMAdgA2tsq+fFXX5lP/ezr8z0vvuiMrjk4Op2f//Nv5rZfuTN/cPcTmZlbWOMqAQAAAIBzIYwB2EAGB7rzGz/0kvzRP3xFrh3sO6Nr9h2byr/8s/tz26/cmf96z5OZnRfKAAAAAMBGIowB2IBeefXOfOKnX5t/dvv16e1sO6Nrnjk6mX/637+RN/3qZ/MnX3k6c0IZAAAAANgQhDEAG1RHWyU/+fqr8/l/8sb85OuvypaOMwtlnjw8kf/7o/flLb/+ufzZ3z4jlAEAAACAdSaMAdjgdvR25p/d/vx87r235d2vuTJd7Wf2V/fe4fH8zH/7Wl73y5/J73z20RybmF3jSgEAAACAWoQxAE1id39X/tXbb8jn3ntb3vWqK9LZdmZ/hT97bCrvv+OhvOL9n8q//LNv5JGhsTWuFAAAAABYThgD0GT2DHTn57/rxtz5c2/Ij7z8snS0FWd03eTsfP7g7ifz5l/7bN71+/fkcw8fTFmWa1wtAAAAACCMAWhSF23bkn//vS/Ip3/2DfmhWy9NW+XMQpkkufNbB/NjH7onb/n1z+W/fOmJTM7Mr2GlAAAAALC5CWMAmtylO3ryi9//wnz6Z1+f73/pJTmLTCaPDI3lX/zp/XnF+z+VX7zjoTx7dHLtCgUAAACATaowogbOT1EUNya5f+n4/vvvz4033riOFbHZPXZwLL9156P5+H3PZmZu4ayubasUeeuNF+Qdt1yS1167+6y6bQAAAABgI3vggQdy0003LV+6qSzLBxrxbGEMnCdhDBvV8Nh0/vBLT+Yjdz+Rg6PTZ339BQPd+f6bL84P3HxprtzVuwYVAgAAAEDjCGOgiQlj2Oim5+bzF1/flw/dtTf3PzNyTve49YrtecfNl+ZtL7wwfV3tq1whAAAAAKw9YQw0MWEMzaIsy9z7xJF86G/25q8e2J+Fc/jrv6ezLbffdGF+8JZL8rIrd6QojDEDAAAAoDmsZxjj/94MsEkURZFbr9iRW6/YkacOT+Qjdz+RP7rnyYxOzZ3xPSZm5vOxrz6dj3316Vy+syc/8NJL8v03X5KLtm1Zw8oBAAAAoLnpjIHzpDOGZjY+PZePffXp/P5dj2fv8Pg53aMokldcuTNve+GFeeuNF2R3f9cqVwkAAAAA58+YMmhiwhhawcJCmbseHc5H7306f/nA/szMLZzTfSpF8nLBDAAAAAAbkDAGmpgwhlZzbHI2f37fs/novU/lvqePnfN9BDMAAAAAbCTCGGhiwhha2cMHRvPRe5/Kn/7tMxkemznn+whmAAAAAFhvwhhoYsIYNoPZ+YV85qGhfPQrT+czDw1lbuHc/91RKZKXXbkjb7nhgrzp+sFcsat3FSsFAAAAgNqEMdDEhDFsNsNj0/mzv30mf3zvU3n4wNh53++qXb154/WDeeP1g7nlih3pbK+sQpUAAAAAcDJhDDQxYQybVVmWeXDfaD7xjX35i2/sy97h8fO+Z39Xe1573a7c9rzB3Hb9YHb1GWcGAAAAwOpYzzCmvREPAaD1FEWRGy4ayA0XDeRnv+O6VQlmRqfn8olv7M8nvrE/RZG88JJtedNi18yNFw2kKIpV/lMAAAAAwNrTGQPnSWcMnGx5x8wnvrEvj61Cx0ySDPZ35dXX7Mqrrt6ZV1+zKxdt27Iq9wUAAABgczCmDJqYMAbqW6tgJkmu3NV7PJh55VU7s723c9XuDQAAAEDrMaYMgJa0cpTZQ/tH88kHDuTTDx3IfU8fO6977x0ez97h8fyXLz2ZokhuvGggr756V151za7cesX29HT6VxwAAAAAG4POGDhPOmPg3Bwcnc6d3xrKpx8ayue/PZyx6blVu3dHW5GXXLY9r7xqZ1525Y68+NJt6e0SzgAAAABsZjpjANh0dvd35R23XJp33HJpZuYW8uXHD+fTDw3lMw8Nnfc4s9n5MvfsPZx79h5OkrRVitx00UBuuWJHbr1ie26+fEd293etxh8DAAAAAE5LZwycJ50xsPr2Do/n0w8N5dMPHcg9ew9ndn71/1115a7e3HrF9sWAZkeu2NmToihW/TkAAAAAbAzr2RkjjIHzJIyBtTU6NZsvPXY4dz06nLseGc7DB8bW5Dm7+rpy6xXb89LLtudFl27LTRcPeO8MAAAAQAsxpgwA6ujv7sibb9iTN9+wJ0kyNDqVLz56KHc9Mpy7HjmUZ45Orspzhsemc8f9+3PH/fuTJJUiuW5Pf15y2ba86JJtedGl23LtYF/a2yqr8jwAAAAANg9hDABNZbC/O9/94ovz3S++OGVZ5snDE7nrkWo484VHh3NkYnZVnrNQJg/tH81D+0fzR/c8lSTZ0tGWF1y8NS+6dGtedGk1pLlk+xbjzQAAAAA4JWEMAE2rKIpcvrM3l+/szQ+//LIsLJR5cP9IvvDIoXxp7+Hc+8ThHF2lcCZJJmfnc8/jh3PP44ePr+3s7cyNF2/NjRcNLP5szeU7elKpCGgAAAAAqBLGANAyKpUiN160NTdetDX/8HVXZWGhzGPDY/ny40fy5b2H8+UnDuepw6sz1mzJofGZfO7hg/ncwwePr/V1tef5F/bnxou25obFkObawf50thtxBgAAALAZCWMAaFmVSpFrBvtzzWB/3vmyy5Ik+49N5d4nDufex4/ky48fzoP7RrJQru5zx6bnqgHQ40eOr3W2VXLtnr7j3TPPu6A/11/Qn209nav7cAAAAAA2HGEMAJvKBVu78/YXXpS3v/CiJMno1Gy++uTR3Pv44XztqaP52lNHMzo1t+rPnZlfyAPPjuSBZ0eSPH18fbC/K8+7oD/P29Nf/bygP9cO9mdLZ9uq1wAAAADA+hDGALCp9Xd35PXX7c7rr9udJFlYKPP4ofF87amjue+po/na08fy4LMjmZlfWJPnD41OZ2h0Op//9vDxtaJILt/RsyykGch1e/py+c5eo84AAAAAmpAwBgCWqVSKXLW7L1ft7sv3vfSSJMn03Hwe2je6LKA5mscOjq9ZDWWZPH5oIo8fmshfPXDg+HpbpcjlO3ty9e6+XDPYt+yzN/3dHWtWDwAAAADnRxgDAKfR1d6WF126LS+6dNvxtWOTs/nG08dy/7PHFsePHcve4fGUq/z+meXmF8o8dnA8jx0cz19/88BJe3sGulYENH25andv9vR3p1Ip1q4oAAAAAE5LGAMA52Drlo685tpdec21u46vjU/P5aH91ffCPPDMSB7YdywP7x9bsxFnyx0Ymc6Bkenc9cihk9a7Oyq5YmdvrtzVmyt29ebKndXPK3b1ZHdfV4pCUAMAAACw1oQxALBKervac/PlO3Lz5TuOr83OL+SRobHj3TMPPDuSb+0fzbHJ2YbUNDW7kIf2j+ah/aPP2evras/lO3tODml29uSyHT3Z3S+oAQAAAFgtwhgAWEMdbZU8/8KBPP/CgfzAzdV30JRlmaHR6Xxr/2j150D189tDo5maXfsumiVj03OLIdHIc/a6Oyq5dHs1mLlsMaBZ+rl0R0+6O9oaVicAAABAsxPGAECDFUWRPQPd2TPQndddt/v4+vxCmScPT+Rb+0fz8IETQc3e4fHML6zhy2hqmJpdyLeHxvLtobGa+4P9XcfDmUu2b8nF27fkku09uXjblly4rTtd7cIaAAAAgCXCGADYINoqRa7cVX2/y1tvuuD4+vTcfJ44NJFHh8byyNBYHjk4lkcPjuXRofFMzs6vS61Do9MZGp3OvU8cec5eUVTDmou3bcnF2xfDmm1bcsn2LYvfe7KlU1gDAAAAbB7CGADY4Lra23Ldnv5ct6f/pPWFhTL7RqaqAc1QNaB5ZGgsjx0cy/DYzDpVm5RlcmBkOgdGpvPVJ4/WPGdbT0cu3LolF23tzoXbuqvflz63bsmerV26awAAAICWIYwBgCZVqRTV7pNtW/L6ZePOkuTYxGz2HhrP48Pj2Ts8nscXvz82PJ7Rqbl1qviEoxOzOToxmwf3Pfd9NUt29XUtBjTVkGbPQHcu2NpV/RzozgVbu9PT6X/KAAAAABuf32AAQAva2tORF/dsy4sv3XbSelmWOTw+k8cPjWfv8EQ1rFkMap44NJGx6fUPapYMj01neGw6X3/6WN1z+rvbjwczSyHNnoGu4+/kGRzoyq6+rnS0VRpYOQAAAMDJhDEAsIkURZGdfV3Z2deVmy/fcdJeWZY5MjGbJw9P5MnDE3nq8ESePDRx/HjfsckslOtUeB2jU3MZnRrLt4fG6p5TFMmOns7s7u/K4EB3Bvu7TvwcP+7O7v4u77IBAAAA1oQwBgBIUg1qdvR2Zkdv53M6apJkZm4hzx6dPB7OPHl4Ik8fmcgzRybz9JHJHBpfv/fUnEpZJofGZ3JofCYP7R895bl9Xe3Z1VcNbnb1nfipHndmV39Xdi+uCW4AAACAMyWMAQDOSGd7JVfs6s0Vu3pr7k/MzOXZo9Vg5pmlzyOT1cDm6GSGRqdTbrDOmpXGpucyNj2Xxw9NnPbcpeBmZ19XdvZ2ZmdfZ3b2dlU/V6xt7+lIu1FpAAAAsGkJYwCAVdHT2Z5rBvtzzWB/zf3pufnsOzqVZ49NZt/Rqew7Nplnj01l39HJ7Ds2lWeOTmZ0auO8s+Z0zia4KYpk25aO7Ozryo7ezuzs7cz23s7s6Kl+nnzckZ29Om8AAACglQhjAICG6GpvO2VnTVINOPYdPRHSLH3uH5nK0Mh09o9M5djkbAOrXh1lmRyZmM2RiTOvvbujcjys2dHbmW09ndne03H8c3tPZ7Ytfm7v6cy23o70d7WnKIo1/JMAAAAA50IYAwBsGH1d7bl2T3+u3VO7uyZJJmfmc2BkKvtHpqqfx058PzAynf3HpjI0OpXZ+Q0+E+00pmYX8uyxqTx7bOqMr2mvFNm2GNhs29KRbT0d2bqlc/GzY9nn8v2O9Hd3pK0ixAEAAIC1IowBAJrKls7Td9gsLJQ5OjmbodFqR83Q6PTx7wfHpnNwZPF4dDoTM/MNrH5tzS2UGR6byfDYzFldVxTJQHc1mBnY0p6tWzqO/wx0d2Rg6fuy9epeewa2dKTD+3AAAADglIQxAEDLqVSK7Fgc73X9Bac+d2x6LkMj1WBmeGw6w6PTi4FG9fjg4vHBsenMzC005g/QYGWZHJucPecRcFs62jKwpf14cNPfvfS9+tl/0vf26vHiZ393e3o624xXAwAAoKUJYwCATa2vqz19u/ty1e6+U55XlmVGp+eq4cxiQHNofPFzbDqHx2dyaGwmw+PV70fP4v0wzW5ydj6Ts/M5MDJ9TtdXiup/DkvhzInQpj19i6FNdb/9+HnLj/sWP7vaK0IdAAAANiRhDADAGSiKotrt0d2Rq08T3CTJ7PxCjozP5NBiSLMU3BwZn8nhiZnje0fGZ3JkYiaHx2ey0NyvuTlnC2UyMjWXkam587pPR1txPJzp7ayGNb1d1Z/+xc++xZ/e4yFOW3o7T1yzdE53h2AHAACA1SOMAQBYAx1tlQwOdGdwoPuMzl9YKDMyNZvDi+HMobHFz8Uum2poM5ujE9X1oxOzOTo5m/nNmuDUMDtf5sjEbI6sQldSpUh6O9vT09VWDXQ629Pb1Za+rvb0LIY2vZ2Le11t2dJZPe5ZPG/ps7ezOoatV+cOAADApiaMAQDYACqVItt6OrOtp/OMr1lYqI5OqwY0s4shzUyOjM8efwfM0YmZHF38fmwxwDk6sXm7cM7UQpmMTs9ldHouybmNX1tpKeDZ0tmWns4TAc7S8fK9nsUQZ+m8nqXzOqp7Wzor1fWO6rqgBwAAYGMTxgAANKlKpcjWLR3ZuqUjl+888+sWFsqMzczl2MRSYDObo5MzOTY5m5HJueNBzsjkbEamTgQ7S2uCnHNzcsCzuipFsqXjRHDT09mW7o62xbXFn8Xj43srz1tc6+5oS3dH5cS1HdU1gQ8AAMC5E8YAAGwylcqJ999cepbXlmWZsekTgc3o1FxGlj6nqmHOyNRsRpd9rx7PHT/faLXVt1Am4zPzGZ+ZX7NnFEXS3X4ioOlaDGyWhzddHW2L51TS3X4i9Olqr1SDnvYT53e1Vz9PPj6x1tFWWbM/CwAAQKMJYwAAOGNFUaS/uyP93R25ZPvZX1+WZSZn5zM2NZeRqbmMLgY1oyd9n13cq34fn5nL2FS1o2Rsai5j03OZWMPQgdrKMpmcnc/kbGP+2bdVinS3VxYDnmpA07n42XUGn10dy763L1trrwZJx9cWQ6HOxe+d7ZW0VXQAAQAAq0sYAwBAwxRFsfg+lPYMDpz7febmFzI+PZ/R6dmMTT83rBmfroY549NzEM/ujgAAIABJREFUGZ858X1sei5j0/PLvs9lZm5h9f6ArJr5hXLNu33q6Wgrjoc11ZBmMbzpqCxbO7Hf2VYNejrb2hY/K8s+29K17Hh56LN07YlnVE5ab9cdBAAALUMYAwBA02lvq2RrTyVbezrO+14zcwvHQ5vx6fnFz8Xv03OZmKkGONXPpYBnaa+6PrF43dKnSWzNbXa+zOz8XMam17eOSpFlgc3J4c9SaNPRVqSzva0a/Czb72gv0tm2GPq0FcvOX3GPthNrK/c62op0LN53+TkdbYX3BwEAwFkSxgAAsKlVf0ndme29natyv7IsM70Y8EzMzB8PeSZn5jM2PZfJ2cUQZ3q++jk7d+L7THVvcvG6yZkT65Oz85mdl/JsJgtlMjW7kKnZhSRz613OSToXQ5mOpaBmRYCztNbRvuJ4ab99xXGN60/+fqq9E/fsaCvSUal+b69U142dAwBgIxDGAADAKiqKYvFF9G3Zucr3np1fqL63ZVlIMzW79P3E+uTsfKYWz5tc3F86npidz9Ti+ol7zWVqdqFh74Oh+c3ML2RmPkkTvL+pUlS76aqj3xaDm0pxUmDTsXyvrUh75UTI0750/spzFtfbl9Yr1c/l60v3ql63/PuJZ7cve17de1Z0IwEANDthDAAANImlXxoPdJ//eLZalrp6ppYFNUshzVKYMzW3tH7y3tL36dn556xNzc5nem7h+PXTswuZmptPqdGHBlgoq+MIm/39UO2V4nhwszzAaaucCHfaj39Wg5y249ecCIfaKtXAp+2ktRNdRNW9EyHQSXtL1y9e016p1vH/t3fncbJU5cHHf8/cy0W2gCKo7JsScEFBIWIiqBjjEkWjqElM3PNmdd+SGDQmLohvXBI1rxLRJCZoTIJLNG7gAlFQIi4oCggiKKsg+7135nn/qOo71Wd6qZ6Z7uk78/t+PvVhzqmzVXNPd3U/VafWrZv/e32xr5PeplGus3/dTPN4DDhJkqTVzWCMJEmSJKD7rp5dxtxXZrJxtlqC6446cHP75vkgzh2b67xGMKfz3zsa6WbZ5n/v2DxXb1Xwp1lf2hptnks2zyWwev8NzwRVcKcTrOkEn7YEj5qBnJmFgZ11ffLr8utmWLh/XWN/lH1U/53p0ee6nvuj0UbVXmdcMxFddZv1yvrrDExJkrQqGYyZIhFxIHAksBewAfgZ8D3g7My8fQXHFcDhwP2B3evsq4DzgfMyl++axojYFXgIcCCwA3ALcDFwVmZet1z9SJIkaWVFBNuuX8e269fBduO506eXThCoCurMB3A2bu4O4mwsAjl3bJ6t8+bzN87Ozf+3a393G1vyN81Wy3ttnmPOu4KkBeayWgKP6V/9buw6gamZmfq/9XJ7zYDNgqBOdAeKyrLrZrqDQt31mA9YRXegaF10/90MHnX63JJX7183Q93XzJa/1zXaKMe0bktdGn/Pl51p7u/R1pb2AwNZkqSpZTBmCkTE8cCrqQIevdwcEacCr83Mayc4rm2AFwAvBPbsU+zHEfFW4O2ZuWkJfR0G/CXwOGCmR5HZiPgE8OrM/OZi+5EkSdLa1hUEutPKjWPz7NyWwMzGIoDTye8Ecpp5zTrdZbv3bSrLzzbyNs+xaTbr/qoA0ebZzl0XkqZBd2Bq9d4NNQ4zQY8gTzNoQ1de5++ZoCugVQaJZhYEfuaDTs3AUARddzg1++tZt9Hngrr1sURjLFHnzZep2pr/e75Mp/0tr0mn7+gu06w/UxxvZ0wzMf869WrbIJgkDWcwZgVFxLbAKcBvDSm6I/BHwFMj4smZ+cUJjG1v4HTgAUOK7gWcDDw9Ip6QmVcsoq8X1G0M+ve4Dng88JiIeHFmvmPUfiRJkqRpsb5+MPz2G1Z6JPNm55JNjaDNptkqfUcd3CkDPJvr/RuL8t15VX6n3uZOenaOTc39s/N9bG6kO/10AkabZucMGkkaaC5hbjYB3ysmbV0jqDUzIDDU3Lfg7+jOnwm2BJGabc80glnNgNCWMo06VZvlPubbLNtv7J8PUi0sV44zYlA7zddlYdnm/k5b3e3O799Sdoau8sPa62qjUbdfeUnLz2DMComIGeA04AnFrlngR8CNwP7Azo19uwGfjIjjMvN/xji23YEzqJYKa7oNuITqzpX96b6W8AjgjIg4epS7dyLixcBbeuz6CXAlsAdwj0b+euDtERGZ+fa2/UiSJEkarLrSunpm0DSbq5+d0gz8bG4EguaDQrkluNPct3muuW+OjbNZB4l67a/SGzdX/50PDM1tGcPm2WTT3HwbVbrT73y9TiBp1mCSpFVqdi7rVQZ9n1sNhgaDGvsGlSmDPbGg/ML6BEPb69Vn9OgzmA9ebRkvC4NYW8oP7KdKl23HguMEynosLNerXvRpt2tcLHzt+42rfO1nAmDh67TrjhvYfoOhgnHzFV45L2NhIObdwOsy80rYErB5AvBWYJ+6zPbAhyLiPpl545jGdirdgZjbgVcC78nMW+ux7QA8H3g980GZewL/QHUHy1ARcTRwUpF9JvCSzDyvUe6BVHfOHNMo95aI+EpmntPukCRJkiStBjMzwYaZYMP6XqsbT79OMKkT9Nk8O1fdlbQloLMwgLO5yGuW7ywx12yvU2d2bm6+3JZ2Gumufc16dX/Nvhv7OmNo1uukJUmrg8G1teXdv30Ev3afu6/0MFY9gzEroH5I/Z8V2a/KzDc2MzJzDviPiDgH+DKwX71rL+DFwIljGNuvAo9uZG0CHlUujZaZtwB/ExHnAZ8BOk9d/fWIeFhmntGiuzdTLT/W8THgyZm5sejra/W4/h14bJ29vq7fDNBIkiRJ0lTbEkzq+ajMrVvmfFBm81wyW98l1AngzM2xILCzeW4+sLMw0JPMZhVUmi/f/O/CINNsVv3ONsYyO9tdvmxnrsc4ZheUm9vS7pZxddKN8pIkbY1mXJluIgzGrIyXAzs10l8E3tSvcGZeERHPBT7byH5RRLw9M69b5rG9rki/cdAzajLzCxHxJuDPG9l/BTxkUCcR8Wjg6EbWdcBzykBMo5+NEfFs4AJg1zr7oRHxyMz8zKC+JEmSJEnjFxGsXxesn+6V7sYmM5nLKuDUCTyVQZ0tWydYNJvMFQGdZv257F2mbKfsZ67HvrL8XCOw1Llja27Lfpidm2M22RKsmptjS/BprtHGgna3pOs25qrXpVOu05ckaXrM+JygiTAYM2H10mPPKrJfk5kDz0Qy83MR8SXgV+qsnYATgHct49juCxzZyLqF6u6TYU4CXgTsUKePjohDMvO7A+o8t0j/XWZeM6iTzLw6It4JvLpox2CMJEmSJGlFVQ/6hnUznWjUGo1KtdQMGHWCOvMBn/ngz1wjkDTXCOw0A0HdZXrX6+5rYf9Vusgv6s919pfj2fJ33Xaz3UaAK3O+7SwCVHPZ6L+om3UfZb3c0g+NMVavwVx2yqz0/2lJW4OZ1XfD7lQyGDN5RwO7NdKXUD0npY1TmA/GABzPMgZjWPgMmw9l5k3DKmXmTRHxYeCZxdh6BmMiYlvgUUX2P7Qc4z/QHYx5dERs6HdHjSRJkiRJmj4zM8EMwTbGrMaqc8dWM8CzJb0lCDQf5FkYzOkVYGJLAKvTVifQ1AkMzTUDRY1+5+Z6/93pqxkUS+b7bo6jq68e45jvs7uP3vUa+xrjbb4emfOvRzWuTv3i2PrV69SZW1i2Wb/5/0qatPDOmIkwGDN5jy3Snxl2V0yzbJE+NiJ2qJ/fshzKsX16hLqfoTsY8zjgDX3KHsv8XTQAF2bmZW06ycxLI+IHwD3rrJ2onhvj3TGSJEmSJEkN83ds+UPr1qQr6NMVuKkDOXMLAzmzjXILyncFoRqBowX7ugNDZf+dwFIZ9MoBfXbVaQS8oCxP1/75fnqXSboDWrNzANXxJQuDXgtfm87Y5/upXvNGv422OgG7alzdr0Gz7WF9d9VrvJZle73KNV/j5eY7xGQYjJm8+xfps9tWzMwrI+JSYL86awNwKHDuUgcVVfjzfosdG3BWkT4sIqJPoGnRr0Gjr3s20vfHYIwkSZIkSZJWgZk6eLbOn8jVRzM4k8zfUdXM6wTuygDRlgBQI9C06w7brujxrBUGYybvkCJ9wYj1L2A+GNNpb8nBGGBfYPtG+pbM/FHbypl5WUTc2mhjB2BvoFcby/EaDGpPkiRJkiRJklaliCACZuqAnUs+bh18NM8ERcR2wD5F9uUjNlOWP3jxIxrYzqjj6lWn39iW2te4XgNJkiRJkiRJkpadd8ZM1l3pXoJvE3D1iG1cUaR3X9KI+rfz40W0cQXdgZF+Y1tqX+N6DYiI3YHdRqx24HL1L0mSJEmSJElafQzGTNaORfrWPs9UGeSWIW0uVtlO2U8bbce21L7G9RoA/AFw4jK2J0mSJEmSJEla41ymbLLKoMHti2jjtiFtLtYkx7bUvsb1GkiSJEmSJEmStOwMxkzWnYr0xkW0cUeR3m6RYylNcmxL7Wtcr4EkSZIkSZIkScvOZcomq7wDZMMi2th2SJuLNcmx3Q5sv4S+xvUaALwT+PCIdQ4ETl/GMUiSJEmSJEmSVhGDMZN1c5Eu7xBpo7wLpGxzsSY5tpvpDsaM2te4XgMy82rg6lHqRMRydS9JkiRJkiRJWoVcpmyyyqDB9jH6L/k7DGlzscp2yn7aaDu2pfY1rtdAkiRJkiRJkqRlZzBmsq4FspHeBth9xDb2LNIj3cUxQNnOXotoo+3YltrXuF4DSZIkSZIkSZKWncGYCcrM24AfFdn7jNhMWf57ix9RlwuL9N6LaKOs029sZV/T8hpIkiRJkiRJkrTsDMZMXhk4OHTE+ocMaW+xLgNua6R3iIh921auyzafA3MLcHmf4tP6GkiSJEmSJEmStOwMxkzeN4r00W0rRsQ9gP0aWZuAC5ZhTGRmAt8ssluPDXhIkf5m3WYvi34N+vRVtidJkiRJkiRJ0tQwGDN5Hy/Sx0VEtKz7q0X6jMxczofXl2N75Ah1y7IfG1D2TKo7Zzru1fYunIjYD7hnI+umuj1JkiRJkiRJkqaSwZjJOxu4tpE+ADi2Zd3nFOnTl2NADR8t0k+JiB2HVYqInYCnFNl9x5aZtwOfLrKf3WqEC8t9KjM3tqwrSZIkSZIkSdLEGYyZsMycA04tsk8cdndMRDwC+JVG1k3Ah5Z5bN8Ezm1k7Qi8vEXVlwM7NNJfycxhy6edUqT/MCJ2G1QhInYH/mBIO5IkSZIkSZIkTRWDMSvjTUBzebFjgFf0KxwRewLvLbLflpnX9irfqJfFdmyLsf1FkX5lRDx0QB+9xv7nwzrJzE8AX2lk7QqcEhHb9OlnA1XgZddG9pcy87+H9SVJkiRJkiRJ0koyGLMC6iDK64vsN0TEOyNij05GRMxExPFUS5vt1yh7JfCWMY3tU3QvIbYN8N8R8YKI2L4xth0i4oXAp+oyHf+VmZ9r2d3LgLlG+teBT0fE4c1CEXFEPabHNbJnaXfXjiRJkiRJkiRJK8pgzMp5E/DxIu/3gR9FxMURcR5wHfAfwD6NMrcBJ2TmDWMc2+8AP2yk7wS8Fbg2Ir4dEd+heu7N39T7Oi4Gntm2k8z8MvCqIvtY4OsRcUVEfC0irgS+RnX3UNPLM/MrSJIkSZIkSZI05QzGrJD62TFPAf612LUOOAB4ALBLse864DGZedaYx3YV8DDg/GLXdsC9gUPpDsIAfAN4WGZeM2JfJwEvpbrTpWkP4AjgHkX+LPCizPy/o/QjSZIkSZIkSdJKMRizgjLz9sx8OvBkqmBGP7cA7wQOzcwzJzS2y4AjqZ4Hc+WAoldSLRd2VGZevsi+3gI8EPgE3cuWNc1R3Ul0RGa+dTH9SJIkSZIkSZK0Etav9AAEmfkR4CMRcRBwFLAnsAG4AfgucFZm3r6IdmOJ49oInBQRJ1PdpXIYsHu9+2qqANJ59V0+S5KZ3wAeFxF3BX6Z6u6gHagCURdTvQbXLrUfSZIkSZIkSZImzWDMFMnMi4CLVnocpTrYcm69jbuva4H/HHc/kiRJkiRJkiRNisuUSZIkSZIkSZIkjZHBGEmSJEmSJEmSpDEyGCNJkiRJkiRJkjRGBmMkSZIkSZIkSZLGyGCMJEmSJEmSJEnSGBmMkSRJkiRJkiRJGiODMZIkSZIkSZIkSWNkMEaSJEmSJEmSJGmMDMZIkiRJkiRJkiSNkcEYSZIkSZIkSZKkMTIYI0mSJEmSJEmSNEYGYyRJkiRJkiRJksZo/UoPQFoFNjQTF1100UqNQ5IkSZIkSZLUR4/fbjf0KjcOkZmT6ktalSLi8cDpKz0OSZIkSZIkSdJInpCZH51ERy5TJkmSJEmSJEmSNEYGYyRJkiRJkiRJksbIZcqkJYqInYFjGlmXAxtXaDiTdiDdS7Q9Abh4hcYiaTKc99La4pyX1h7nvbT2OO+ltWWtz/kNwN6N9Bcy88ZJdLx+Ep1Iq1k9WSeyruC0iYgy6+LM/M5KjEXSZDjvpbXFOS+tPc57ae1x3ktri3MegP9diU5dpkySJEmSJEmSJGmMDMZIkiRJkiRJkiSNkcEYSZIkSZIkSZKkMTIYI0mSJEmSJEmSNEYGYyRJkiRJkiRJksbIYIwkSZIkSZIkSdIYGYyRJEmSJEmSJEkaI4MxkiRJkiRJkiRJY2QwRpIkSZIkSZIkaYwMxkiSJEmSJEmSJI2RwRhJkiRJkiRJkqQxWr/SA5C0VbsGeG2RlrS6Oe+ltcU5L609zntp7XHeS2uLc36FRGau9BgkSZIkSZIkSZJWLZcpkyRJkiRJkiRJGiODMZIkSZIkSZIkSWNkMEaSJEmSJEmSJGmMDMZIkiRJkiRJkiSNkcEYSZIkSZIkSZKkMTIYI0mSJEmSJEmSNEYGYyRJkiRJkiRJksbIYIwkSZIkSZIkSdIYGYyRJEmSJEmSJEkaI4MxkiRJkiRJkiRJY2QwRpIkSZIkSZIkaYwMxkiSJEmSJEmSJI2RwRhJkiRJkiRJkqQxMhgjSZIkSZIkSZI0RutXegCStk4RcSBwJLAXsAH4GfA94OzMvH0lxyatdhERwH7Afanm4C7AHVTz8AfAucs9DyNiJ+AhwL2AXwBuAy6jmvNXLnNf9waOAO4BrAOuA74NfDUzNy9nX5J6i4g7AUcDvwjcGdgI/JhqHl6yzH15TiG1FBEHA4dRzZftqT6PrwK+D5yfmXcsoW3nvTQlImJb4AHAIVTzcTvg58DVwHnARZmZy9DPeuAo4D7ArsAs8BPg65n5naW2X/S1J/BgYF/mj+f7wJcz8+bl7EtaC1bj5/Ykj2mlxDK8d0taQyLieODVwOF9itwMnAq8NjOvndS4pNUuIu4MHA/8GvBw4K4Dim8CPgG8NTO/sMR+9wf+EjiB6qSrlMAXgBMz84tL6CeAZwGvoAr49HId8C7gjZl5y2L7klabiPgX4GlF9mWZud8i2toNOBF4JrBDn2JfB16XmaeP2n7Rl+cUUgv1BRF/DDwX2H9A0Y3AOcC/ZebbRmjfeS9NiYg4AngR8GRg2wFFrwBOAd6Wmdcvop8dgVcCvw/cpU+xC4E3AacuJfATEccArwGO7VNkI3Aa8BeZeeli+5FWWh1wPJIqwHkk8EBgp0aRRZ2f9+hn1X1uT/KYVprBGEmt1FfmnAL8Vssq1wBPXsqPs5IqEfF3VD/A9AqGDPMB4I8z8+eL6PcE4H1UV94Ok8BJwKtG/bIWEbsAHwIe2bLKJcDjl/tqPWlrFBG/Dny0x66Rv+xFxLHAhxkc7G36APC8zNw4Yj+eU0gtRcTjgPcCdxuh2lWZefeW7R+L815acRExA7weeBmjPVLgKuCZmfmpEfq6L3A6g4O7Tf8NPDUzbxxhXJ2Lrd5EdUxt3AL8bmZ+ZJR+pJUUEQ8BXkIVgNljSPElB2NW4+f2pI5pWhiMkTRUfWL478ATil2zwI+AG6lO5HYu9t8KHJeZ/zP2QUqrWER8jWrZrlJnGYGrgG2obvkv5yFUV8k+YpTb/yPiKcC/svDL4DXA5cDuwJ5AFPvfmpkvGqGf7YAzqa4catoIXEq1/NoBLLw65hrg6My8qG1f0moTETsD36Gai6WRvuxFxC8Dn6ZaNqTpBuCHVMsE7E21dGDTv1N98Wr1pcJzCqm9iHgR8BYWftbeDlwJXEs1Z+9B9w8YrYIxzntpekTEe6guvirdClxMtSThrlTnxeV7wkbg+Mz8ZIt+Dga+zMIfPW+muuBpO6rlkLcp9v8P8PBRliOKiHcAf1RkJ9WSQ9cA+/QYxyzwlMz8j7b9SCspIl4I/E3L4ksKxqzGz+1JHdM0GSXaLmntehkL34TfDeyTmQdk5gOobm1+EtUbc8f2wIfqH4skLY8bgHcCjwXunJl7Z+YDM/Mwqi9oDwO+VNQ5kurW4Vbq9WDfR/d5wvlUX8B2z8wjMnNvqjWs/72o/sKIeNIIx/N/6Q7EzAGvA+6emQdn5v2o3l+eRbUubcduVO8v5UmZtJa8mflAzKKX7quXQTyN7i9Bl1EtjXiXzDw8M/en+nHm74vqT6JaTqUtzymkFiLiOVSfkc0fXT8JPBrYJTMPzMyjMvN+mbkb1XvBM4CPUP0wO6x95700JSLiySwMxFxAdb6/cz3Pj8rMg6jukjuR7nm+AXh/Pa8H9bOehVefXw/8LtW8Pywz7wXcHfhrqvPyjgdT3QXf9phOYGEg5iPAwZm5T/19YjfgOOCbjTLr6mPZr21f0hRbtmchrcbP7Qkf0/TITDc3N7e+G9WPuz+nuoKls71yQPk9qaLXzfKvXenjcHPbmjfga/W8eg6wXYvy66hOVrLYHtayvw8W9c4BfqFP2ejR10XA+hb9/CKwuaj79AHl700VkGmWf9ZK//9xc1uJjWrd9bl6HsxSfWlqzo1LR2jr9UXdS4A9BpT/06L8DVTB4WH9eE7h5tZiAw6iugq+8+9+46DPxx7128xH572b25RswLeKf/PnAjsMqfNwqudENuu9akid5xflrwcOHVD+N4vym4B7tjieDT3m8buoV+fpUX7n+pib5d+/0v9f3NzabMAL63+zPwfOoApaPplq1YpjF3t+3qOfVfe5PaljmrZtxQfg5uY23RvVGq/NN7sv9DuJatR5RFHn58CuK30sbm5b60Z1VdyGEeus6/Gl5p9b1Ls31Q+7nTp3AIcMqXMn4PtFX89r0ddpRZ0PtKjznPKEFthmpf8fublNcqO6euyixjx462K/7FHdZXZTUfcRQ+pEfT7QrPPXLfrynMLNrcUGfL74d/+UZW7fee/mNiUb1bJjWWwPaln33UW9sweU3UB1BXuz/LNb9PGPi/g+8ftFne8DdxpS51Cq7x2dOpuBX1zp/z9ubsM24MD63+9Mj32LOj/v0c6q+9ye5DFN2+YyZZL6qteJfFaR/Zqs3wX7yczP0b1M0k7ACcs8PGnNyMxP5IgPp8vMWRYuJfCoFlWfTffyZP+amd8d0tftwBuL7F5rXm9R35LcXM4sgde0GN/7qG5d7tiXankDaS15HdUXP6h+WPnzJbT1NGDHRvqL9ed4X/V5wGuL7GfXD+rtyXMKqZ2IeALVkqMdH87MDy9zN857aXocXKR/nJnntqxbPuj+oAFlH0X13IWOS6nOq4d5DdV5esdTWixBVH4PeEMOedZMZl5AdaFWxzoWvn9IUyczL87MCzJzbnjpRVuNn9sTOaZpZDBG0iBHU0WrOy6hetB2G6cU6eOXY0CSRlI+O2bXiNh+SJ3HF+lyLvdzGt3PrHhQROwxoPxjgfWN9JmZecmwTuqT3PKLo+8vWjMi4kFUyyF0/GFmLmU96nI96LZz/gyqJQk67g780oDynlNI7Ty/SJc/OiwH5700Pe5SpC8foe6PivQuA8qW8/59w35gheqHZqor0Tu2AR7Tr3xE7AUc3si6GfjQsH5q5bwvxyytVavxc3tSxzR1DMZIGuSxRfozbU7YOmWL9LERscMyjElSez/rkdf3SraIOJjuK+puAc5u01FmlmWDhe8hTeW+T7fpp1a+vzxuhLrSVisitqH6orKuzvpwZn58Ce3tCDy0yG41F+vzgc8W2YPmoucU0hARsSfdd7F+IzO/s8x9OO+l6XJjkd6uZ6neyrLXDig7qXPvsp+z6u8JbZwF3NpIHxwR92xZV1q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      "text/plain": [
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     },
     "metadata": {
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     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 绘制 loss 曲线\n",
    "import matplotlib.pyplot as plt\n",
    "# 如果绘制图表不清晰，可以设置高 dpi 来提高图表清晰度\n",
    "plt.rcParams['figure.dpi'] = 300\n",
    "\n",
    "# 绘制 loss 图像\n",
    "plt.figure()\n",
    "plt.plot(np.linspace(0, 1000, 100), network.loss_history)\n",
    "plt.title('Neural Network Loss vs. Epochs')  # 给 x 轴添加标签\n",
    "plt.xlabel('Epoch')  # 给 x 轴添加标签\n",
    "plt.ylabel('loss')  # 给 y 轴添加标签\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Emily: 0.969\n",
      "Frank: 0.054\n"
     ]
    }
   ],
   "source": [
    "# 试试预测结果\n",
    "emily = np.array([-7, -3]) # 128 pounds, 63 inches\n",
    "frank = np.array([20, 2])  # 155 pounds, 68 inches\n",
    "\n",
    "print(\"Emily: %.3f\" % network.feedforward(emily)) # 0.969 - 女性\n",
    "print(\"Frank: %.3f\" % network.feedforward(frank)) # 0.969 - 男性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ndarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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